{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".. meta::\n",
    "   :description: A guide which introduces the most important steps to get started with pymoo, an open-source multi-objective optimization framework in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".. meta::\n",
    "   :keywords: Multi-objective Optimization, Python, Evolutionary Computation, Optimization Test Problem, Hypervolume"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. _nb_getting_started:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we like to introduce *pymoo* by presenting an example optimization scenario. This guide goes through the essential steps to get started with our framework. This guide is structured as follows:\n",
    "\n",
    "1. Introduction to Multi-objective Optimization and an exemplarily Test Problem\n",
    "2. Implementation of a Problem (vectorized, element-wise or functional)\n",
    "3. Initialization of an Algorithm (in our case NSGA2)\n",
    "4. Definition of a Termination Criterion\n",
    "5. Optimize (functional through `minimize` or object-oriented by calling `next()`)\n",
    "6. Visualization of Results and Convergence\n",
    "7. Summary\n",
    "8. Source code (in one piece)\n",
    "\n",
    "\n",
    "We try to cover the essential steps you have to follow to get started optimizing your own optimization problem and have also included some posteriori analysis which is known to be particularly important in multi-objective optimization.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-Objective Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, multi-objective optimization has several objective functions with subject to inequality and equality constraints to optimize <cite data-cite=\"multi_objective_book\"></cite>. The goal is to find a set of solutions that do not have any constraint violation and are as good as possible regarding all its objectives values. The problem definition in its general form is given by:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align}\n",
    "\\begin{split}\n",
    "\\min \\quad& f_{m}(x) \\quad \\quad \\quad \\quad m = 1,..,M  \\\\[4pt]\n",
    "\\text{s.t.}   \\quad& g_{j}(x) \\leq 0  \\quad \\; \\; \\,  \\quad j = 1,..,J \\\\[2pt]\n",
    "\\quad& h_{k}(x) = 0        \\quad  \\; \\; \\quad k = 1,..,K \\\\[4pt]\n",
    "\\quad& x_{i}^{L} \\leq x_{i} \\leq x_{i}^{U}  \\quad i = 1,..,N \\\\[2pt]\n",
    "\\end{split}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The formulation above defines a multi-objective optimization problem with $N$ variables, $M$ objectives, $J$ inequality and $K$ equality constraints. Moreover, for each variable $x_i$ lower and upper variable boundaries ($x_i^L$ and $x_i^U$) are defined."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Problem\n",
    "\n",
    "In the following, we investigate exemplarily a bi-objective optimization with two constraints. \n",
    "We tried to select a suitable optimization problem with enough complexity for demonstration purposes, but not too difficult to lose track of the overall idea. Its definition is given by:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align} \n",
    "\\begin{split}\n",
    "\\min \\;\\; & f_1(x) = (x_1^2 + x_2^2) \\\\ \n",
    "\\max \\;\\; & f_2(x) = -(x_1-1)^2 - x_2^2 \\\\[1mm]\n",
    "\\text{s.t.} \\;\\; & g_1(x) = 2 \\, (x_1 - 0.1) \\, (x_1 - 0.9) \\leq 0\\\\ \n",
    "& g_2(x) = 20 \\, (x_1 - 0.4) \\, (x_1 - 0.6) \\geq 0\\\\[1mm] \n",
    "& -2 \\leq x_1 \\leq 2 \\\\\n",
    "& -2 \\leq x_2 \\leq 2\n",
    "\\end{split}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " It consists of two objectives ($M=2$) where $f_1(x)$ is minimized and $f_2(x)$ maximized. The optimization is with subject to two inequality constraints ($J=2$) where $g_1(x)$ is formulated as a less than and $g_2(x)$ as a greater than constraint. The problem is defined with respect to two variables ($N=2$), $x_1$ and $x_2$, which both are in the range $[-2,2]$. The problem does not contain any equality constraints ($K=0$). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hide_input"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 351,
       "width": 494
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X1, X2 = np.meshgrid(np.linspace(-2, 2, 500), np.linspace(-2, 2, 500))\n",
    "\n",
    "F1 = X1**2 + X2**2\n",
    "F2 = (X1-1)**2 + X2**2\n",
    "G = X1**2 - X1 + 3/16\n",
    "\n",
    "G1 = 2 * (X1[0] - 0.1) * (X1[0] - 0.9)\n",
    "G2 = 20 * (X1[0] - 0.4) * (X1[0] - 0.6)\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "levels = [0.02, 0.1, 0.25, 0.5, 0.8]\n",
    "plt.figure(figsize=(7, 5))\n",
    "CS = plt.contour(X1, X2, F1, levels, colors='black', alpha=0.5)\n",
    "CS.collections[0].set_label(\"$f_1(x)$\")\n",
    "\n",
    "CS = plt.contour(X1, X2, F2, levels, linestyles=\"dashed\", colors='black', alpha=0.5)\n",
    "CS.collections[0].set_label(\"$f_2(x)$\")\n",
    "\n",
    "plt.plot(X1[0], G1, linewidth=2.0, color=\"green\", linestyle='dotted')\n",
    "plt.plot(X1[0][G1<0], G1[G1<0], label=\"$g_1(x)$\", linewidth=2.0, color=\"green\")\n",
    "\n",
    "plt.plot(X1[0], G2, linewidth=2.0, color=\"blue\", linestyle='dotted')\n",
    "plt.plot(X1[0][X1[0]>0.6], G2[X1[0]>0.6], label=\"$g_2(x)$\",linewidth=2.0, color=\"blue\")\n",
    "plt.plot(X1[0][X1[0]<0.4], G2[X1[0]<0.4], linewidth=2.0, color=\"blue\")\n",
    "\n",
    "plt.plot(np.linspace(0.1,0.4,100), np.zeros(100),linewidth=3.0, color=\"orange\")\n",
    "plt.plot(np.linspace(0.6,0.9,100), np.zeros(100),linewidth=3.0, color=\"orange\")\n",
    "\n",
    "plt.xlim(-0.5, 1.5)\n",
    "plt.ylim(-0.5, 1)\n",
    "plt.xlabel(\"$x_1$\")\n",
    "plt.ylabel(\"$x_2$\")\n",
    "\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.12),\n",
    "          ncol=4, fancybox=True, shadow=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above shows the contours of the problem. The contour lines of the objective function $f_1(x)$ is represented by a solid and $f_2(x)$ by a dashed line. The constraints $g_1(x)$ and $g_2(x)$ are parabolas which intersect the $x_1$-axis at $(0.1, 0.9)$ and $(0.4, 0.6)$. A thick orange line illustrates the pareto-optimal set. Through the combination of both constraints, the pareto-set is split into two parts.\n",
    "Analytically, the pareto-optimal set is  given by $PS = \\{(x_1, x_2) \\,|\\, (0.1 \\leq x_1 \\leq 0.4) \\lor (0.6 \\leq x_1 \\leq 0.9) \\, \\land \\, x_2 = 0\\}$ and the Pareto-front by $f_2 = (\\sqrt{f_1} - 1)^2$ where $f_1$ is defined in $[0.01,0.16]$ and $[0.36,0.81]$.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Implementation of a Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In *pymoo*, we consider **minimization** problems for optimization in all our modules. However, without loss of generality, an objective that is supposed to be maximized can be multiplied by $-1$ and be minimized. Therefore, we minimize $-f_2(x)$ instead of maximizing $f_2(x)$ in our optimization problem. Furthermore, all constraint functions need to be formulated as a $\\leq 0$ constraint. \n",
    "The feasibility of a solution can, therefore, be expressed by:\n",
    "\n",
    "$$ \\begin{cases}\n",
    "\\text{feasible,} \\quad \\quad \\sum_i^n \\langle g_i(x)\\rangle = 0\\\\\n",
    "\\text{infeasbile,} \\quad \\quad \\quad \\text{otherwise}\\\\\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\text{where} \\quad \\langle g_i(x)\\rangle =\n",
    "\\begin{cases}\n",
    "0, \\quad \\quad \\; \\text{if} \\; g_i(x) \\leq 0\\\\\n",
    "g_i(x), \\quad  \\text{otherwise}\\\\\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "\n",
    "For this reason, $g_2(x)$ needs to be multiplied by $-1$ in order to flip the $\\geq$ to a $\\leq$ relation. We recommend the normalization of constraints to give equal importance to each of them. \n",
    "For $g_1(x)$, the coefficient results in $2 \\cdot (-0.1) \\cdot (-0.9) = 0.18$ and for $g_2(x)$ in $20 \\cdot (-0.4) \\cdot (-0.6) = 4.8$, respectively. We achieve normalization of constraints by dividing $g_1(x)$ and $g_2(x)$ by its corresponding coefficient.\n",
    "\n",
    "\n",
    "\n",
    "Finally, the optimization problem to be optimized using *pymoo* is defined by:\n",
    "\n",
    "\\begin{align} \n",
    "\\label{eq:getting_started_pymoo}\n",
    "\\begin{split}\n",
    "\\min \\;\\; & f_1(x) = (x_1^2 + x_2^2) \\\\ \n",
    "\\min \\;\\; & f_2(x) = (x_1-1)^2 + x_2^2 \\\\[1mm] \n",
    "\\text{s.t.} \\;\\; & g_1(x) = 2 \\, (x_1 - 0.1) \\, (x_1 - 0.9)  \\, /  \\,  0.18 \\leq 0\\\\ \n",
    "& g_2(x) = - 20 \\, (x_1 - 0.4) \\, (x_1 - 0.6) \\,  /  \\,  4.8 \\leq 0\\\\[1mm] \n",
    "& -2 \\leq x_1 \\leq 2 \\\\\n",
    "& -2 \\leq x_2 \\leq 2\n",
    "\\end{split}\n",
    "\\end{align}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "This getting started guide demonstrates **3** different ways of defining a problem:\n",
    "\n",
    "- **By Class**\n",
    "    - **Vectorized evaluation:** A set of solutions is evaluated directly.\n",
    "    - **Elementwise evaluation:** Only one solution is evaluated at a time.\n",
    "- **By Functions**: Functional interface as commonly defined in other optimization libraries.\n",
    "\n",
    "**Optional**: Define a Pareto set and front for the optimization problem to track convergence to the analytically derived optimum/optima.\n",
    "\n",
    "Please choose the most convenient implementation for your purpose."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining a problem through a class allows defining the problem very naturally, assuming the metadata, such as the number of variables and objectives, are known.\n",
    "The problem inherits from the [Problem](problems/index.ipynb) class. By calling the `super()` function in the constructor `__init__` the problem properties such as the number of variables `n_var`, objectives `n_obj` and constraints `n_constr` are supposed to be initialized. Furthermore, lower `xl` and upper variables boundaries `xu` are supplied as a NumPy array. Please note that most algorithms in our framework require the lower and upper boundaries to be provided and not equal to negative or positive infinity. Finally, the evaluation function `_evaluate` needs to be overwritten to calculated the objective and constraint values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vectorized Evaluation\n",
    "\n",
    "The `_evaluate` method takes a **two-dimensional** NumPy array `X` with *n* rows and *m* columns as an input. Each row represents an individual, and each column an optimization variable. After doing the necessary calculations, the objective values must be added to the dictionary out with the key `F` and the constraints with key `G`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: This method is only called once per iteration for most algorithms. This gives you all the freedom to implement your own parallelization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pymoo.model.problem import Problem\n",
    "\n",
    "class MyProblem(Problem):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__(n_var=2, \n",
    "                         n_obj=2, \n",
    "                         n_constr=2, \n",
    "                         xl=np.array([-2,-2]), \n",
    "                         xu=np.array([2,2]))\n",
    "\n",
    "    def _evaluate(self, X, out, *args, **kwargs):\n",
    "        f1 = X[:,0]**2 + X[:,1]**2\n",
    "        f2 = (X[:,0]-1)**2 + X[:,1]**2\n",
    "        \n",
    "        g1 = 2*(X[:, 0]-0.1) * (X[:, 0]-0.9) / 0.18\n",
    "        g2 = - 20*(X[:, 0]-0.4) * (X[:, 0]-0.6) / 4.8\n",
    "        \n",
    "        out[\"F\"] = np.column_stack([f1, f2])\n",
    "        out[\"G\"] = np.column_stack([g1, g2])\n",
    "        \n",
    "        \n",
    "vectorized_problem = MyProblem()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Elementwise Evaluation\n",
    "\n",
    "\n",
    "The `_evaluate` method takes a **one-dimensional** NumPy array `x` number of entries equal to `n_var`. This behavior is enabled by setting `elementwise_evaluation=True` while calling the `super()` method.\n",
    "\n",
    "**Note**: This method is called in each iteration for **each** solution exactly once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pymoo.util.misc import stack\n",
    "from pymoo.model.problem import Problem\n",
    "\n",
    "class MyProblem(Problem):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__(n_var=2, \n",
    "                         n_obj=2, \n",
    "                         n_constr=2, \n",
    "                         xl=np.array([-2,-2]), \n",
    "                         xu=np.array([2,2]),\n",
    "                         elementwise_evaluation=True)\n",
    "\n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        f1 = x[0]**2 + x[1]**2\n",
    "        f2 = (x[0]-1)**2 + x[1]**2\n",
    "        \n",
    "        g1 = 2*(x[0]-0.1) * (x[0]-0.9) / 0.18\n",
    "        g2 = - 20*(x[0]-0.4) * (x[0]-0.6) / 4.8\n",
    "        \n",
    "        out[\"F\"] = [f1, f2]\n",
    "        out[\"G\"] = [g1, g2]\n",
    "        \n",
    "\n",
    "elementwise_problem = MyProblem()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The definition by functions is a common way in Python and available and many other optimization frameworks. It reduces the problem's definitions without any overhead, and the number of objectives and constraints is simply derived from the list of functions.\n",
    "\n",
    "After having defined the functions, the problem object is created by initializing `FunctionalProblem`. Please note that the number of variables `n_var` must be passed as an argument. \n",
    "\n",
    "**Note**: This definition is recommended to be used to define a problem through simple functions. It is worth noting that the evaluation can require many functions calls. For instance, for 100 individuals with 2 objectives and 2 constraints 400 function calls are necessary for evaluation. Whereas, a vectorized definition through the `Problem` class requires only a single function call. Moreover, if metrics are shared between objectives or constraints, they need to be calculated twice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pymoo.model.problem import FunctionalProblem\n",
    "\n",
    "objs = [\n",
    "    lambda x: x[0]**2 + x[1]**2,\n",
    "    lambda x: (x[0]-1)**2 + x[1]**2\n",
    "]\n",
    "\n",
    "constr_ieq = [\n",
    "    lambda x: 2*(x[0]-0.1) * (x[0]-0.9) / 0.18,\n",
    "    lambda x: - 20*(x[0]-0.4) * (x[0]-0.6) / 4.8\n",
    "]\n",
    "\n",
    "functional_problem = FunctionalProblem(2, \n",
    "                                       objs, \n",
    "                                       constr_ieq=constr_ieq, \n",
    "                                       xl=np.array([-2,-2]), \n",
    "                                       xu=np.array([2,2]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Optional) Pareto front (pf) and Pareto set (ps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we have a test problem where the optimum is **known**. For illustration, we like to measure the convergence of the algorithm to the known true optimum. Thus, we implement override the `_calc_pareto_front` and `_calc_pareto_set` for this purpose. Please note that both have to be mathematically derived. \n",
    "\n",
    "**Note: This is not necessary if your goal is solely optimizing a function**. For test problems, this is usually done to measure and visualize the performance of an algorithm.\n",
    "The implementation of `func_pf` and `func_ps` looks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.util.misc import stack\n",
    "\n",
    "def func_pf(flatten=True, **kwargs):\n",
    "        f1_a = np.linspace(0.1**2, 0.4**2, 100)\n",
    "        f2_a = (np.sqrt(f1_a) - 1)**2\n",
    "        \n",
    "        f1_b = np.linspace(0.6**2, 0.9**2, 100)\n",
    "        f2_b = (np.sqrt(f1_b) - 1)**2\n",
    "        \n",
    "        a, b = np.column_stack([f1_a, f2_a]), np.column_stack([f1_b, f2_b])\n",
    "        return stack(a, b, flatten=flatten)\n",
    "    \n",
    "def func_ps(flatten=True, **kwargs):\n",
    "        x1_a = np.linspace(0.1, 0.4, 50)\n",
    "        x1_b = np.linspace(0.6, 0.9, 50)\n",
    "        x2 = np.zeros(50)\n",
    "        \n",
    "        a, b = np.column_stack([x1_a, x2]), np.column_stack([x1_b, x2])\n",
    "        return stack(a,b, flatten=flatten) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This information can be passed to the definition via class or functions as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add to Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pymoo.util.misc import stack\n",
    "from pymoo.model.problem import Problem\n",
    "\n",
    "class MyTestProblem(MyProblem):\n",
    "\n",
    "    def _calc_pareto_front(self, *args, **kwargs):\n",
    "        return func_pf(**kwargs)\n",
    "\n",
    "    def _calc_pareto_set(self, *args, **kwargs):\n",
    "        return func_ps(**kwargs)\n",
    "    \n",
    "test_problem = MyTestProblem()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add to Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.model.problem import FunctionalProblem\n",
    "\n",
    "\n",
    "functional_test_problem = FunctionalProblem(2,\n",
    "                                            objs,\n",
    "                                            constr_ieq=constr_ieq,\n",
    "                                            xl=-2,\n",
    "                                            xu=2,\n",
    "                                            func_pf=func_pf,\n",
    "                                            func_ps=func_ps\n",
    "                                            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize the object\n",
    "\n",
    "Choose the way you have defined your problem and initialize it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = test_problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, we would like to mention that in many test optimization problems, implementation already exists. For example, the test problem *ZDT1* can be initiated by:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.factory import get_problem\n",
    "zdt1 = get_problem(\"zdt1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our framework has various single- and many-objective optimization test problems already implemented. Furthermore, a more advanced guide for custom problem definitions is available. In case problem functions are computationally expensive, more sophisticated parallelization of the evaluation functions might be worth looking at.\n",
    "\n",
    "\n",
    "[Optimization Test Problems](problems/index.ipynb) | \n",
    "[Define a Custom Problem](problems/custom.ipynb) | \n",
    "[Parallelization](problems/parallelization.ipynb) |\n",
    "[Callback](interface/callback.ipynb) |\n",
    "[Constraint Handling](misc/constraint_handling.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Initialization of an Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, we need to initialize a method to optimize the problem.\n",
    "In *pymoo*, factory methods create an `algorithm` object to be used for optimization. For each of those methods, an API documentation is available, and through supplying different parameters, algorithms can be customized in a plug-and-play manner.\n",
    "Depending on the optimization problem, different algorithms can be used to optimize the problem. Our framework offers various [Algorithms](algorithms/index.ipynb), which can be used to solve problems with different characteristics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, the choice of a suitable algorithm for optimization problems is a challenge itself. Whenever problem characteristics are known beforehand, we recommended using those through customized operators.\n",
    "However, in our case, the optimization problem is rather simple, but the aspect of having two objectives and two constraints should be considered. We decided to use [NSGA-II](algorithms/nsga2.ipynb) with its default configuration with minor modifications. We chose a population size of 40 (`pop_size=40`) and decided instead of generating the same number of offsprings to create only 10 (`n_offsprings=10`). Such an implementation is a greedier variant and improves the convergence of rather simple optimization problems without difficulties regarding optimization, such as the existence of local Pareto fronts.\n",
    "Moreover, we enable a duplicate check (`eliminate_duplicates=True`), making sure that the mating produces offsprings that are different from themselves and the existing population regarding their design space values. To illustrate the customization aspect, we listed the other unmodified default operators in the code snippet below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.algorithms.nsga2 import NSGA2\n",
    "from pymoo.factory import get_sampling, get_crossover, get_mutation\n",
    "\n",
    "algorithm = NSGA2(\n",
    "    pop_size=40,\n",
    "    n_offsprings=10,\n",
    "    sampling=get_sampling(\"real_random\"),\n",
    "    crossover=get_crossover(\"real_sbx\", prob=0.9, eta=15),\n",
    "    mutation=get_mutation(\"real_pm\", eta=20),\n",
    "    eliminate_duplicates=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `algorithm` object contains the implementation of NSGA-II  with the custom settings supplied to the factory method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Definition of a Termination Criterion\n",
    "\n",
    "Furthermore, a termination criterion needs to be defined to start the optimization procedure finally. Different kind of [Termination Criteria](interface/termination.ipynb) are available. Here, since the problem is rather simple, we run the algorithm for some number of generations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.factory import get_termination\n",
    "\n",
    "termination = get_termination(\"n_gen\", 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of the number of generations (or iterations), other criteria such as the number of function evaluations or the improvement in design or objective space between generations can be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Optimize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we are solving the problem with the algorithm and termination criterion we have defined. \n",
    "In *pymoo*, we provide two interfaces for solving an optimization problem:\n",
    "\n",
    "- **Functional:** Commonly in Python, a function is used as a global interface. In pymoo, the `minimize` method is the most crucial method which is responsible for using an algorithm to solve a problem using\n",
    "other attributes such as `seed`, `termination`, and others.\n",
    " \n",
    "- **Object Oriented:** The object-oriented interface directly uses the algorithm object to perform an iteration.\n",
    "This allows the flexibility of executing custom code very quickly between iterations. However, features already \n",
    "implemented in the functional approach, such as displaying metrics, saving the history, or pre-defined callbacks, need to be incorporated manually.\n",
    "\n",
    "Both ways have their benefits and drawbacks depending on the different use cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functional Interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The functional interface is provided by the `minimize` method. By default, the method performs deep-copies of the algorithm and the termination object. Which means the objects are not altered during the function call. This ensures repetitive function calls end up with the same results. The `minimize` function returns the [Result](interface/result.ipynb) object, which provides attributes such as the optimum. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "n_gen |  n_eval |   cv (min)   |   cv (avg)   |     igd      |      gd      |      hv     \n",
      "==========================================================================================\n",
      "    1 |      40 |  0.00000E+00 |  2.36399E+01 |  0.355456661 |  0.005362269 |  0.259340233\n",
      "    2 |      50 |  0.00000E+00 |  1.15486E+01 |  0.355456661 |  0.005362269 |  0.259340233\n",
      "    3 |      60 |  0.00000E+00 |  5.277918607 |  0.355456661 |  0.005362269 |  0.259340233\n",
      "    4 |      70 |  0.00000E+00 |  2.406068542 |  0.336177786 |  0.003791374 |  0.267747228\n",
      "    5 |      80 |  0.00000E+00 |  0.908316880 |  0.146187673 |  0.030825699 |  0.326821728\n",
      "    6 |      90 |  0.00000E+00 |  0.264746300 |  0.146187673 |  0.030825699 |  0.326821728\n",
      "    7 |     100 |  0.00000E+00 |  0.054063822 |  0.127166639 |  0.048434536 |  0.326821728\n",
      "    8 |     110 |  0.00000E+00 |  0.003060876 |  0.116635438 |  0.040756917 |  0.333328216\n",
      "    9 |     120 |  0.00000E+00 |  0.00000E+00 |  0.114104403 |  0.033780540 |  0.333467251\n",
      "   10 |     130 |  0.00000E+00 |  0.00000E+00 |  0.113905849 |  0.079757906 |  0.336916611\n",
      "   11 |     140 |  0.00000E+00 |  0.00000E+00 |  0.108326595 |  0.061746460 |  0.350973236\n",
      "   12 |     150 |  0.00000E+00 |  0.00000E+00 |  0.075994278 |  0.028407765 |  0.409709713\n",
      "   13 |     160 |  0.00000E+00 |  0.00000E+00 |  0.075900587 |  0.028006615 |  0.410363369\n",
      "   14 |     170 |  0.00000E+00 |  0.00000E+00 |  0.060504000 |  0.025567286 |  0.411662871\n",
      "   15 |     180 |  0.00000E+00 |  0.00000E+00 |  0.049446911 |  0.016289051 |  0.418530080\n",
      "   16 |     190 |  0.00000E+00 |  0.00000E+00 |  0.045458331 |  0.011383437 |  0.420489384\n",
      "   17 |     200 |  0.00000E+00 |  0.00000E+00 |  0.039469853 |  0.009911055 |  0.430486842\n",
      "   18 |     210 |  0.00000E+00 |  0.00000E+00 |  0.029430084 |  0.005849149 |  0.436066481\n",
      "   19 |     220 |  0.00000E+00 |  0.00000E+00 |  0.026914825 |  0.006440139 |  0.438071834\n",
      "   20 |     230 |  0.00000E+00 |  0.00000E+00 |  0.025819848 |  0.006597745 |  0.439045983\n",
      "   21 |     240 |  0.00000E+00 |  0.00000E+00 |  0.025538102 |  0.006092177 |  0.439313321\n",
      "   22 |     250 |  0.00000E+00 |  0.00000E+00 |  0.025430023 |  0.005831189 |  0.439357583\n",
      "   23 |     260 |  0.00000E+00 |  0.00000E+00 |  0.022907750 |  0.005325638 |  0.441118548\n",
      "   24 |     270 |  0.00000E+00 |  0.00000E+00 |  0.020729474 |  0.005408909 |  0.444667855\n",
      "   25 |     280 |  0.00000E+00 |  0.00000E+00 |  0.020685726 |  0.004700076 |  0.447352036\n",
      "   26 |     290 |  0.00000E+00 |  0.00000E+00 |  0.018021642 |  0.004673768 |  0.449131574\n",
      "   27 |     300 |  0.00000E+00 |  0.00000E+00 |  0.017734569 |  0.004981929 |  0.452104699\n",
      "   28 |     310 |  0.00000E+00 |  0.00000E+00 |  0.017256071 |  0.004906941 |  0.452453391\n",
      "   29 |     320 |  0.00000E+00 |  0.00000E+00 |  0.017099845 |  0.004821622 |  0.452586422\n",
      "   30 |     330 |  0.00000E+00 |  0.00000E+00 |  0.015604763 |  0.004715009 |  0.454343077\n",
      "   31 |     340 |  0.00000E+00 |  0.00000E+00 |  0.015494765 |  0.004509278 |  0.454689229\n",
      "   32 |     350 |  0.00000E+00 |  0.00000E+00 |  0.015494765 |  0.004509278 |  0.454689229\n",
      "   33 |     360 |  0.00000E+00 |  0.00000E+00 |  0.013269463 |  0.002882294 |  0.456133797\n",
      "   34 |     370 |  0.00000E+00 |  0.00000E+00 |  0.012326693 |  0.002872865 |  0.456726256\n",
      "   35 |     380 |  0.00000E+00 |  0.00000E+00 |  0.011929203 |  0.002718091 |  0.456859586\n",
      "   36 |     390 |  0.00000E+00 |  0.00000E+00 |  0.011452732 |  0.002668863 |  0.459103818\n",
      "   37 |     400 |  0.00000E+00 |  0.00000E+00 |  0.011187256 |  0.002467009 |  0.459278838\n",
      "   38 |     410 |  0.00000E+00 |  0.00000E+00 |  0.010961294 |  0.002442439 |  0.459731947\n",
      "   39 |     420 |  0.00000E+00 |  0.00000E+00 |  0.010982275 |  0.002847599 |  0.459564223\n",
      "   40 |     430 |  0.00000E+00 |  0.00000E+00 |  0.010485987 |  0.002833493 |  0.460033718\n"
     ]
    }
   ],
   "source": [
    "from pymoo.optimize import minimize\n",
    "\n",
    "res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Result](interface/result.ipynb) object provides the corresponding X and F values and some more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Object-Oriented Interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the contrary, the object-oriented approach directly modifies the algorithm object by calling the `next` method. Thus, it makes sense to create a deepcopy of the algorithm object beforehand, as shown in the code below.\n",
    "In the while loop, the algorithm object can be accessed to be modified or for other purposes.\n",
    "\n",
    "**NOTE**: In this guide, we have used the functional interface because the history is used during analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen: 1 n_nds: 1 constr: 0.0 ideal: [0.43280594 0.12244878]\n",
      "gen: 2 n_nds: 1 constr: 0.0 ideal: [0.43280594 0.12244878]\n",
      "gen: 3 n_nds: 1 constr: 0.0 ideal: [0.43280594 0.12244878]\n",
      "gen: 4 n_nds: 2 constr: 0.0 ideal: [0.43280594 0.09718357]\n",
      "gen: 5 n_nds: 3 constr: 0.0 ideal: [0.13614243 0.09718357]\n",
      "gen: 6 n_nds: 3 constr: 0.0 ideal: [0.13614243 0.09718357]\n",
      "gen: 7 n_nds: 4 constr: 0.0 ideal: [0.13614243 0.08467286]\n",
      "gen: 8 n_nds: 5 constr: 0.0 ideal: [0.02736815 0.08467286]\n",
      "gen: 9 n_nds: 6 constr: 0.0 ideal: [0.02347838 0.08420915]\n",
      "gen: 10 n_nds: 6 constr: 0.0 ideal: [0.02317765 0.08420915]\n",
      "gen: 11 n_nds: 7 constr: 0.0 ideal: [0.02317765 0.08420915]\n",
      "gen: 12 n_nds: 7 constr: 0.0 ideal: [0.02317765 0.08420915]\n",
      "gen: 13 n_nds: 7 constr: 0.0 ideal: [0.02317765 0.08420915]\n",
      "gen: 14 n_nds: 9 constr: 0.0 ideal: [0.02317765 0.05440163]\n",
      "gen: 15 n_nds: 10 constr: 0.0 ideal: [0.02317765 0.05440163]\n",
      "gen: 16 n_nds: 11 constr: 0.0 ideal: [0.02317765 0.0166032 ]\n",
      "gen: 17 n_nds: 14 constr: 0.0 ideal: [0.02317765 0.0166032 ]\n",
      "gen: 18 n_nds: 16 constr: 0.0 ideal: [0.02150969 0.0166032 ]\n",
      "gen: 19 n_nds: 18 constr: 0.0 ideal: [0.02150969 0.0166032 ]\n",
      "gen: 20 n_nds: 20 constr: 0.0 ideal: [0.02150969 0.0166032 ]\n",
      "gen: 21 n_nds: 22 constr: 0.0 ideal: [0.02150969 0.0166032 ]\n",
      "gen: 22 n_nds: 24 constr: 0.0 ideal: [0.02150969 0.01627147]\n",
      "gen: 23 n_nds: 27 constr: 0.0 ideal: [0.02150969 0.01320869]\n",
      "gen: 24 n_nds: 28 constr: 0.0 ideal: [0.02006779 0.01320869]\n",
      "gen: 25 n_nds: 28 constr: 0.0 ideal: [0.02006779 0.01320869]\n",
      "gen: 26 n_nds: 30 constr: 0.0 ideal: [0.02006779 0.01320869]\n",
      "gen: 27 n_nds: 33 constr: 0.0 ideal: [0.01896005 0.01320869]\n",
      "gen: 28 n_nds: 36 constr: 0.0 ideal: [0.01896005 0.01320869]\n",
      "gen: 29 n_nds: 37 constr: 0.0 ideal: [0.01896005 0.01320869]\n",
      "gen: 30 n_nds: 38 constr: 0.0 ideal: [0.01896005 0.01320869]\n",
      "gen: 31 n_nds: 40 constr: 0.0 ideal: [0.01896005 0.01200888]\n",
      "gen: 32 n_nds: 40 constr: 0.0 ideal: [0.01896005 0.01200888]\n",
      "gen: 33 n_nds: 40 constr: 0.0 ideal: [0.01896005 0.01200888]\n",
      "gen: 34 n_nds: 40 constr: 0.0 ideal: [0.01805568 0.01200888]\n",
      "gen: 35 n_nds: 40 constr: 0.0 ideal: [0.01805568 0.01200888]\n",
      "gen: 36 n_nds: 40 constr: 0.0 ideal: [0.0163747  0.01200888]\n",
      "gen: 37 n_nds: 40 constr: 0.0 ideal: [0.0163747  0.01180915]\n",
      "gen: 38 n_nds: 40 constr: 0.0 ideal: [0.0163747  0.01180915]\n",
      "gen: 39 n_nds: 40 constr: 0.0 ideal: [0.0163747  0.01180915]\n",
      "gen: 40 n_nds: 40 constr: 0.0 ideal: [0.0163747  0.01180915]\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "\n",
    "# perform a copy of the algorithm to ensure reproducibility\n",
    "obj = copy.deepcopy(algorithm)\n",
    "\n",
    "# let the algorithm know what problem we are intending to solve and provide other attributes\n",
    "obj.setup(problem, termination=termination, seed=1)\n",
    "\n",
    "# until the termination criterion has not been met\n",
    "while obj.has_next():\n",
    "    \n",
    "    # perform an iteration of the algorithm\n",
    "    obj.next()\n",
    "    \n",
    "    # access the algorithm to print some intermediate outputs\n",
    "    print(f\"gen: {obj.n_gen} n_nds: {len(obj.opt)} constr: {obj.opt.get('CV').min()} ideal: {obj.opt.get('F').min(axis=0)}\")\n",
    "    \n",
    "# finally obtain the result object\n",
    "result = obj.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Visualization of Results and Convergence\n",
    "\n",
    "### Results\n",
    "\n",
    "The optimization results are illustrated below (design and objective space). The solid lines represent the analytically derived Pareto set, and front in the corresponding space, and the circles represent solutions found by the algorithm. It can be observed that the algorithm was able to converge, and a set of nearly-optimal solutions was obtained. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 387,
       "width": 498
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymoo.visualization.scatter import Scatter\n",
    "\n",
    "# get the pareto-set and pareto-front for plotting\n",
    "ps = problem.pareto_set(use_cache=False, flatten=False)\n",
    "pf = problem.pareto_front(use_cache=False, flatten=False)\n",
    "\n",
    "# Design Space\n",
    "plot = Scatter(title = \"Design Space\", axis_labels=\"x\")\n",
    "plot.add(res.X, s=30, facecolors='none', edgecolors='r')\n",
    "if ps is not None:\n",
    "    plot.add(ps, plot_type=\"line\", color=\"black\", alpha=0.7)\n",
    "plot.do()\n",
    "plot.apply(lambda ax: ax.set_xlim(-0.5, 1.5))\n",
    "plot.apply(lambda ax: ax.set_ylim(-2, 2))\n",
    "plot.show()\n",
    "\n",
    "# Objective Space\n",
    "plot = Scatter(title = \"Objective Space\")\n",
    "plot.add(res.F)\n",
    "if pf is not None:\n",
    "    plot.add(pf, plot_type=\"line\", color=\"black\", alpha=0.7)\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization is a vital post-processing step in multi-objective optimization. Although it seems to be pretty easy for our example optimization problem, it becomes much more difficult in higher dimensions where trade-offs between solutions are not readily observable. For visualizations in higher dimensions, various more advanced [Visualizations](visualization/index.ipynb) are implemented in our framework."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convergence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A not negligible step is the post-processing after having obtained the results. We strongly recommend not only analyzing the final result but also the algorithm's behavior. This gives more insights into the convergence of the algorithm.\n",
    "\n",
    "For such an analysis, intermediant steps of the algorithm need to be considered. This can either be achieved by:\n",
    "\n",
    "- A `Callback` class storing the necessary information in each iteration of the algorithm.\n",
    "- Enabling the `save_history` flag when calling the minimize method to store a deepcopy of the algorithm's objective each iteration.\n",
    "\n",
    "We provide some more details about each variant in our [convergence](misc/convergence.ipynb) tutorial.\n",
    "As you might have already seen, we have set `save_history=True` when calling the `minmize` method in this getting started guide and, thus, will you the `history` for our analysis. Moreover, we need to decide what metric should be used to measure the performance of our algorithm. In this tutorial, we are going to use `Hypervolume` and `IGD`. Feel free to look at our [performance indicators](misc/performance_indicator.ipynb) to find more information about metrics to measure the performance of multi-objective algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a first step we have to extract the population in each generation of the algorithm. We extract the constraint violation (`cv`), the objective space values (`F`) and the number of function evaluations (`n_evals`) of the corresponding generation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_evals = []    # corresponding number of function evaluations\\\n",
    "F = []          # the objective space values in each generation\n",
    "cv = []         # constraint violation in each generation\n",
    "\n",
    "\n",
    "# iterate over the deepcopies of algorithms\n",
    "for algorithm in res.history:\n",
    "    \n",
    "    # store the number of function evaluations\n",
    "    n_evals.append(algorithm.evaluator.n_eval)\n",
    "    \n",
    "    # retrieve the optimum from the algorithm\n",
    "    opt = algorithm.opt\n",
    "    \n",
    "    # store the least contraint violation in this generation\n",
    "    cv.append(opt.get(\"CV\").min())\n",
    "    \n",
    "    # filter out only the feasible and append\n",
    "    feas = np.where(opt.get(\"feasible\"))[0]\n",
    "    _F = opt.get(\"F\")[feas]\n",
    "    F.append(_F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**NOTE:** If your problem has different scales on the objectives (e.g. first objective in range of [0.1, 0.2] and the second objective [100, 10000] you **HAVE** to normalize to measure the performance in a meaningful way! This example assumes no normalization is necessary to keep things a bit simple."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constraint Violation (CV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, in the first generation, a feasible solution was already found.\n",
    "Since the constraints of the problem are rather simple, the constraints are already satisfied in the initial population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First feasible solution found after 40 evaluations\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 413
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "k = min([i for i in range(len(cv)) if cv[i] <= 0])\n",
    "first_feas_evals = n_evals[k]\n",
    "print(f\"First feasible solution found after {first_feas_evals} evaluations\")\n",
    "\n",
    "plt.plot(n_evals, cv, '--', label=\"CV\")\n",
    "plt.scatter(first_feas_evals, cv[k], color=\"red\", label=\"First Feasible\")\n",
    "plt.xlabel(\"Function Evaluations\")\n",
    "plt.ylabel(\"Constraint Violation (CV)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypvervolume (HV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypervolume is a very well-known performance indicator for multi-objective problems. It is known to be pareto-compliant and is based on the volume between a predefined reference point and the solution provided. Hypervolume requires to define a reference point `ref_point` which shall be larger than the maximum value of the Pareto front. \n",
    "\n",
    "**Note:** Hypervolume becomes computationally expensive with increasing dimensionality. The exact hypervolume can be calculated efficiently for 2 and 3 objectives. For higher dimensions, some researchers use a hypervolume approximation, which is not available yet in pymoo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 276,
       "width": 392
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pymoo.performance_indicator.hv import Hypervolume\n",
    "\n",
    "# MODIFY - this is problem dependend\n",
    "ref_point = np.array([1.0, 1.0])\n",
    "\n",
    "# create the performance indicator object with reference point\n",
    "metric = Hypervolume(ref_point=ref_point, normalize=False)\n",
    "\n",
    "# calculate for each generation the HV metric\n",
    "hv = [metric.calc(f) for f in F]\n",
    "\n",
    "# visualze the convergence curve \n",
    "plt.plot(n_evals, hv, '-o', markersize=4, linewidth=2)\n",
    "plt.title(\"Convergence\")\n",
    "plt.xlabel(\"Function Evaluations\")\n",
    "plt.ylabel(\"Hypervolume\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IGD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For IGD the Pareto front needs to be known or to be approximated.\n",
    "In our framework the Pareto front of **test problems** can be obtained by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf = problem.pareto_front(flatten=True, use_cache=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For real-world problems, you have to use an **approximation**. An approximation can be obtained by running an algorithm a couple of times and extracting the non-dominated solutions out of all solution sets. If you have only a single run, an alternative is to use the obtain a non-dominated set of solutions as an approximation. However, the result does then only indicate how much the algorithm's progress in converging to the final set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 276,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pymoo.performance_indicator.igd import IGD\n",
    "\n",
    "if pf is not None:\n",
    "\n",
    "    # for this test problem no normalization for post prcessing is needed since similar scales\n",
    "    normalize = False\n",
    "\n",
    "    metric = IGD(pf=pf, normalize=normalize)\n",
    "\n",
    "    # calculate for each generation the HV metric\n",
    "    igd = [metric.calc(f) for f in F]\n",
    "\n",
    "    # visualze the convergence curve \n",
    "    plt.plot(n_evals, igd, '-o', markersize=4, linewidth=2, color=\"green\")\n",
    "    plt.yscale(\"log\")          # enable log scale if desired\n",
    "    plt.title(\"Convergence\")\n",
    "    plt.xlabel(\"Function Evaluations\")\n",
    "    plt.ylabel(\"IGD\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running Metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way of analyzing a run when the true Pareto front is **not** known is using are recently proposed [running metric](https://www.egr.msu.edu/~kdeb/papers/c2020003.pdf). The running metric shows the difference in the objective space from one generation to another and uses the algorithm's survival to visualize the improvement.\n",
    "This metric is also being used in pymoo to determine the termination of a multi-objective optimization algorithm if no default termination criteria have been defined."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For instance, this analysis reveals that the algorithm was able to improve from the 4th to the 5th generation significantly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAAILCAYAAACzYcnGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdeXydZZ338e99tpycnJO9bdp0oRttUvaWyla7sYiCuDxuMEIFcUBglHF0YIZ5BnUExRFQQHFhTFFEHBHR8RkQKC0ti6W0QGnS0tKWrumSpcnJSc56P3+cJE1yzp1mOclZ8nm/XudFc69XeXX59sp1/X6GaZoCAAAAkJls6R4AAAAAAGsEdgAAACCDEdgBAACADEZgBwAAADIYgR0AAADIYAR2AAAAIIMR2AEAAIAMRmAHAAAAMhiBHQAAAMhgBHYAAAAggxHYAQAAgAxGYAcAAAAymCPdA0gnwzB2SSqUtDvNQwEAAEBuO0lSi2ma0wd745gO7JIK8/PzS6uqqkrTPRAAAADkrrq6OrW3tw/p3rEe2HdXVVWVvvHGG+keBwAAAHLY/PnztXHjxt1DuZc17AAAAEAGI7ADAAAAGYzADgAAAGQwAjsAAACQwQjsAAAAQAYjsAMAAAAZjMAOAAAAZLCxXocdAADkqFgspsbGRrW2tioYDMo0zXQPCTnCMAzl5eXJ5/OptLRUNtvIzoET2AEAQM6JxWLau3evAoFAuoeCHGSapjo6OtTR0aG2tjZNmTJlREM7gR0AAOScxsZGBQIBORwOVVRUqKCgYMRnQTF2xGIxtbW1qb6+XoFAQI2NjSovLx+x9/ErFwAA5JzW1lZJUkVFhXw+H2EdKWWz2eTz+VRRUSHp+K+3EXvfiD4dAAAgDYLBoCSpoKAgzSNBLuv69dX1622kENgBAEDO6dpgysw6RpJhGJI04hua+VUMAAAADEFXYB9pbDodTY07pcN16R5F7jPskq9CGl8lOfLSPRoAAIBhIbCPpqbd0va/pnsUY4fdJU2YJ1XOl8ZVSXZ+uQMAgOzDkhjkrmhIOrBJev0X0l/vkN78jXR4qxSLpXtkAACMqpqaGt15550j9vwlS5bIMIykH4eDCbPh4v8gxoZIu7T3b/GPyytNOjP+KZ0hjdL6MwAA0qWmpkZr1qwZ0dA+ZcoUeTyehOME9uHj/yDGnpBf2r02/nEXS5VnSZPOkoomE94BABiiRx99VEuWLEn3MHISgR1jW0ez9N6q+KdgfGd4PzO+aRUAACADENhHU8l0ac6H0z2K3Nd6UKp/R4qFB3df22Hp3Wfin8LK+Kx75VmSp3RkxgkAwAirqanRF77whe6ve5Yh/OUvf6kVK1akYVQYLAL7aCqdHv9g5EWC8dB+YGO8lKYZHdz9Lfvjn61/lkpOiof3SWdI7qIRGS4AYHTc/ofN6R7CkNz9iVOHdN+KFSu0YsUKLVmyRGvWrEna4Ofqq6/W+vXrB/zMhQsX6tFHH004/uSTT+q2227T1q1bFQ6HNXv2bH3mM5/RrbfeKrfbPaTxI47AjtzkyJMmz49/Qm1S/WZp/xvS0e2SBtmNrGl3/LPlKal8djy8TzxNctHuGgCQ/fbs2aNt27YN+PqKiuTLRl966SXdd999Ou+889Ta2qqamhrdfvvteuqpp/Tiiy+qoIC/N4eKwI7c5yqQpp4T/3S0SAfflPZvlJp2DfJBpnT03fhn83/HGzNNOlOqOJUGTQCArLV69ephP+Puu+9WdXW1iori34l2u936+te/rv379+uHP/yh7rjjDt13333Dfs9YRR12jC3uQmn6B6ULviot/3ep6nKpcPLgn2NGpUPvSJt+JT37r9KGX0oH35Kig1w3DwBADjj33HO7w3pPX/rSlyRJv/rVr5Iux8HAMMOOsctTKs26MP5pPRRf775/Y3zz6WDEwvFZ+4NvSg63VHFafLNq+cmSzT4yYwcAIAvMmDFDhmGooaFBR48e1bhx49I9pKxEYAckyTdBmnOpdPKH4ptN92+MB/j2psE9J9Ih7Vsf/7i80sQz4uGdBk0AgAzVtSF1oBYvXjzgZTSmaTKzngIEdqAnw4g3UCqaHF8u07SrM7xvijdcGoyQX3p/XfzjLoqvd6+cLxVNIbwDQJoMtdpKtjP6+XtnuGvYn3jiCf30pz/VqlWrEs7t3LlTklRWVqby8vJhvWcsI7ADVgwjPjNeOkOa9wmpYXs8vNe/LYUDg3tWxzFp5+r4p2BcPLxPOksqnDgSIwcAoJeSkhJJUkdHh9xut+699169/PLLevLJJ4f97Pb2dr3yyivat2+fJk/uvS/sJz/5iSTpyiuv7PcfDegfm06BgbDZpHFzpDM+J130bensL8YDt901+Ge1HZG2/1Va811p9fek7c9JoUH+AwAAgEFYsGCBJOn555/X0aNHtXLlSvl8vpQ82zAMBYNBffSjH9VLL72ktrY2HTlyRN/73vf08MMP64wzztB//Md/pORdYxUz7MBg2R3xUo4Vp8YbNB3aEq/xfmSrFIsM7lmtB6StB6Td66SFX5KKKkdmzACAMe2WW27Rli1btGLFCoXDYS1atEh33XVXSp595ZVXqqysTI8//rhuuOEG7dmzR6Zp6uSTT9a3vvUtffWrX5XH40nJu8YqAjswHI68+KbSyrPis+TdDZre1aAaNHU0S1v/R/rA34/YUAEAY5fP59Njjz02Is92Op267LLLdNlll43I80FgB1LH5ZGmfiD+CbZKB96MV5pp3Dmw+w/XSZGQ5BjCMhsAAJCzCOzASMjzSdMXxT/tTcfLRB7b189NpuSvl4qnjtowAQBA5iOwAyMtv0SatTz+8R+Ol4jcvTY+C99X6yECOwAA6IUqMcBo8o6XTr4kXo89mdaDozseAACQ8QjsQDr4LOqv+w+N7jgAAEDGI7AD6eCdkPw4M+wAAKAPAjuQDr6K5McDjfFKMQAAAJ0I7EA6OPMld3GSE52VYgAAADoR2IF0sVrH3so6dgAAcByBHUgXH+vYAQDAiRHYgXShUgwAABgAAjuQLlSKAQAAA0BgB9Kl30oxwdEdCwAAyFgEdiBd+q0Uw7IYAEDq1NTU6M477xzx9+zevVvLli2TYRhavXr1Ca9fv369Lr30UhUXF6uwsFBLly7V888/P+LjzDYEdiCdqBQDABgFNTU1+uY3vzlizzdNUw8//LDOOOMMbdiwYUD3/PWvf9X5558vn8+n2tpa7dy5U3PnztXFF1+sX//61yM21mxEYAfSiUoxAIAccNttt+n+++/XM888o7POOuuE17e1temaa67RxIkT9eijj2rSpEkqLy/XQw89pNNOO0033nijDh8+PAojzw4EdiCdqBQDAMgBF154oTZt2qRzzjlnQNc//vjjqq+v15VXXim329193Gaz6ZprrpHf79fPf/7zkRpu1iGwA+lEpRgAwAiqqamRYRhas2aNJMkwjO5PTU1Nyt5z0UUXKT8/f8DX/+Uvf5EknXvuuQnnuo51XQPJke4BAGOa1Qx7oCFeKcaRN7rjAYBc9+evpHsEQ3P5D4d024oVK7RixQotWbJEa9askWmaCddcffXVWr9+/YCfuXDhQj366KNDGk+XzZs3S5JOOumkhHNdx955551hvSOXENiBdHK645ViOpoTz/kPScVTR39MAIAxZc+ePdq2bduAr6+osChLPAj19fWSpJKSkoRzxcXxCmqtra0KBALyeDzDfl+2I7AD6eabmDywtxLYAQAjbyDlF1Otvb1dkuR0OhPOuVyu7h8T2ONYww6kG5ViAABjTNd693A4nHAuFAp1/5iwHkdgB9KNSjEAgDGma1lNU1NTwrnm5vh3nX0+H4G9E4EdSDcqxQAA0mjJkiW9qsec6LNkyZJhv/PUU0+VJO3atSvh3O7duyVJp5xyyrDfkytYwz6K3m9o0/ZD/nQPI+fZbNKk4nzNmeCTYRjpHs6JUSkGAEbPEKutZLv+/j5Mxxr2D3/4w/rjH/+o1157TR/72Md6nXv11Ve7r0EcgX0U7WkM6IWtdO0aLTPHFeizC6fKm5fhv8ypFAMAGGFd1Vg6Ojrkdrt177336uWXX9aTTz6ZlvFceeWVuuOOO/Sb3/xGd955Z3fzpFgsppUrV6qgoEDXX399WsaWiVgSg5z13pE2Pbhqh/Y2BtI9lBOzmmVvZR07AGD4FixYIEl6/vnndfToUa1cuVI+ny9t4ykoKFBNTY0OHjyoq6++WgcOHNDRo0d100036e2339aPf/xjTZhgsWR0DMrwqcfcste/XbtCr6R7GDnPkE1e20SV2E/WsXbpZy/t1BVnTNKCk0rTPTRrvgnSkbrE46xjBwCkwC233KItW7ZoxYoVCofDWrRoke66666UPX/16tVaunRpr2NdXy9evDjpsptLL71Ua9eu1Z133qmqqirFYjGdddZZeuaZZ3TxxRenbGy5gMA+inb567Qj9HS6h5HTzJipjoPtivjDyvN6NHnq2RrvOFO/fWOe9jVN1mWnTZTDnoHfWLKcYa8f3XEAAHKSz+fTY489NmLPX7JkSdIuqidyzjnn6JlnnhmBEeUWAjtyhhkz1fT6UbW916poICK7xyH/zGYdWrhJdsOpzbUna83+D+gr51+mk0rGp3u4vVlVivET2AEAGOsI7KOotMClPIc93cPIWYF9bWrf6Vf73jY5Cp0K7W2TJOVPLlB+paGGaK3WNtbq5f+p0enjTtPFMxZrUeUiVRQMv8XysFEpBgAAWCCwj6KygjzlOTJwOUaOaG+PSh1R5RXnqaOhQ84Sl6KBiCL+3l3UYqapTYffUl3TO/rJmz/RrOJZuqDyAi2avEgnFZ6UnlKQVIoBAAAWCOzIGU6fUw6PQx0NfsXaIoo4DOVPLpDD60x6fUc4qmjM1PbmHdrRvEM1W2pU6a3UosmLdEHlBZpbOlc2YxT/geWbmDywt9YT2AEAGMMI7KNoTukcfXbuZ9M9jJxlzjG1IbJBmzdu1pa/bVHhNJ/yp3vlnpRveU84GlMsZirfZZfNMLTfv1+/3fpb/Xbrb1WeX64LKi/QBZUX6LRxp8lhG+HfLpaVYljHDgDAWEZgH0Wnjztdp487Pd3DyGlfOu1Lqqur03fv+a4uuuYi7c3fqxfeX6djwVbLe6KmqbZgVPkuuxy248thjrYf1R93/FF/3PFH+Vw+nTfpPC2qXKT5FfOVZx+BNeVUigEAAEkQ2JFTDMNQdXW1Zpw0Q59f+nlJ0tcXhvU/217Tb95+VgdCbypoHku4z5SpQCgit8MuV5J9Bq2hVj27+1k9u/tZuR1uLaxYqEWVi/SBiR+Q1+VNzeB9FptfqcUOAMCYRmBHznPanPp41SItmrJQv351t7Y1b9WRyFs6HHlTAfNIr2s7IlFFTVNup11WW087Ih16ad9LemnfS3LYHDpz/JlaNHmRzpt0nkrdw2jO5LUI7O2NVIoBAGAMI7BjzCj35unGpbP0h435envfDM1yfUz+2AEdib6pw5E31RrbLylxXXt/IrGIXq9/Xa/Xv677dJ9OKT9FF0y+YGjlIqkUAwAAkiCwY0zJc9j12bOnaHJJvv73nXr5jEr57JWa4fqIArEj8Zn36Ftqju7sXNduk8M2sEoxpkxtPrpZm49uHnq5SCrFAACAPgjsGHMMw9Ci2eM0sShfv12/R22hqCTJYxunaa4LNU0XKhg7piPRtzuXzeyQcwj9rnYMpVykr8KiUgzr2AEAGKsI7BizZo336uZls/TY3/ZoX1N7r3N5tiJNti3SZOcihc2A3L4dkmebNhxar1A0NOh3DbhcpOXG00ODficAAMgNBHaMacUel770wRl6+s0DeuP9pqTXOA2Pov7TNE4L9PNl/6j3A+9o3f51enn/y2oLtw36nV3lIp969ykZRwyd7DpZnz7r01q6YKkMKsUAAIA+RrGNI5CZnHabPnlWpa44Y5Ls/fyOOOIP6ZG1+1WsU3Xbwtv0hyv+oO8v/r4+OvOjKssvG9Q7zZipw68d1vZntuvp3z6tL3zzC/rez74ns2BC8hu6KsUAAIB+Pf/889q3b9+g73v00UcVjUZHYETDR2AHFF/Xfs6MMl2/aIZ8butvPAUjMT32tz165p2Dssuh+RPm66vzv6onLntCDyx/QJ+Z8xlN8k464fsCBwJq2d4i/16/YuGYWva06Dd//Y3qduyKV4pJxs+yGADA4NXU1OjOO+8clXft3r1by5Ytk2EYWr16db/XGoZh+bnwwgsH/W7TNHXrrbfquuuuk9vttnyn1bieeOIJLV++XI2NjYN+90gjsAM9TCsr0M3LZmlamaff69a8e1S/fGW3AqGIJMlm2DSvbJ7+/vS/168u/ZV+cfEvdM28azSjeEbS+8OtYUUCEbkKXQo2BuUqdOlI4xG9d+A9Op4CAFKqpqZG3/zmN0f0HaZp6uGHH9YZZ5yhDRs2DPi+OXPmJP1MnTr4ymj/8i//oscee0yrV69WeXm5JKmurk7t7e2W92zatKn7x08++aSi0aiuuOIKRSKRQb9/JBHYgT4K3U598YLpOmdG/02Qdhz268FVO3SgufcfBIZhaEbxDF0z7xr94uJf6Ncf/rVuOP0GzSubJ6OzHZPT55TD41CoJaRwa1ihlpAcHoeabc10PAUAZJ3bbrtN999/v5555hmdddZZA75v69atST//9V//Naj3r1+/Xvfcc49uu+02TZ8+XVL8HxGf+cxndMopp+i5557rdf2OHTt04YUX6txzz+1ePuN2u3Xfffdp3bp1uv/++wf1/pFGYAeScNhtuuKMSv2f+ZVy2KzrpzcFwnp4zXvatCf5hlVJmuSdpE/P+bQeWP6Afnf573Tr/FtVVVWlwtmF8k7xyrAb8k7xqnB2odpK25hhBwBknQsvvFCbNm3SOeeck5b3f+c735Hb7daNN97YfcwwDK1du1bXX3+9rr76av3d3/2dJOnBBx/UOeeco6qqKtXV1Wny5Mnd9yxYsEBLlizR97//fYVCg68KN1KoEgP0Y/60Uk0odOvXr+3RsfZw0mvCUVO/27BP+5ra9eFTJ8reT8Avyy/T5TMvl82w6f2W9+Wd6lWkLaJJyyfJU+lRbWOtNO1DyW8msAPAsC373bJ0D2FIVn161aDvqamp0Re+8IXur3s28PvlL3+pFStWpGJokqSLLrooZc8arObmZv3lL3/R0qVLlZ+f3+tcUVGRbrvtNt1888264oorJEmvvfaa1q5dq6qqqqTPW7ZsmVavXq3nnntOH/nIR0Z8/APBDDtwApNLPLp52SzNHFfQ73WvvNegR9btVGtH8mDfU3VZtQzDUMHkArnHuVUwuUCGYejdpncV9pQnv4lKMQCAQVixYoVM09TixYslxZeIdH16hvWrr75ac+fOHfDn6quvTtkYv/Wtb6mqqkoej0fFxcVaunSpnnjiiUE946WXXlI0GlV1dXXCOb/fr3vvvVfV1dWqqIgvOT3nnHN0/vnn6+abb9bu3bsT7pk3b54kadWqwf8jaaQQ2IEB8OY5dO350/XB2RZhutOuowE9+OIO7WkI9HvdtMJpKnAm/gMgFA3pvbZ9Un5J8huZZQcApNiePXu0bdu2AX/27NmTsne//fbb+v3vf6/m5ma98cYbmjJlij772c/qy1/+8oCfUVtbK0maNKl3lTbTNHX++efroYce0i9+8Qs99thjkqSbb75Zf/vb37R161ZVVVVp//79ve7rek5dXZLO42nCkhhggGw2Q5eeOlGVJfl68o19CkXNpNe1tEf087U7dfnpk7RwevKNqzbDpqrSKm04lLiTvrahVnO9FVJ7knXx/kNSybRh/TwAAOjpROUXR8ozzzyjiy++uHupzsyZM7Vy5Upt3rxZP/nJT3TJJZd0L2Ppz4EDByRJxcW9yyIbhqHHH39c06dPT1gqM3v2bD3//PPauHGjKisre50rKirq9dxMwAw7MEinTS7WjUtmqazAZXlNJGbqqU379YeN+xSJxpJeM698XtLjW45uoVIMACDnXXLJJb3W1UvxkH399ddLijcyGohAIP5dbZcr8e/l6urqhLDeU7KKNnl5eZKktrbBdzMfKQR2YAgqity6aekszZng7fe613c36acv7dSxQOK69uqyxLV2klTXWEelGADAmDVz5kxJ8ZKPA+FwxBeMDKRLqWmaWrJkSb/XdD2n67mZIHNGAmSZfJdd15x3kp6vO6xVWw9bXrevqV0Pvrhdn1s4VTPGHQ/4VaXJd6fXt9WrwZWnsmQnCewAMCxDqbaS65YsWaI1a9YM+PrFixeP6DIa00y+5NRK1xKWjo6OlLy/q9FS13MzATPswDAYhqGLqifo8+dMU57D+reTPxjVI+t26eUdR7v/IPK6vJpamLyTW22wIfmDqBQDABikvstO+lq9enWvCjIn+qQirH/lK1/RHXfckfTce++9JyneBXUgZsyIdxU/cuTIsMclSUePHu313ExAYAdSoHpSoW5aOkvjfXmW18RM6X/ePqj/3rBPoUh8XbvVspja5h1UigEApERJSfzvk64Z6HvvvVef/OQn0zkkHTt2TH/6058SlrGYpqmf/vSnktTd6OhEFixYIEnatWtXSsa2c+fOXs/NBAR2IEXG+fJ045KZmjepsN/rNu1t1k/XvKfGtpDmlVlsPG3Ywjp2AEBKdAXP559/XkePHtXKlSvl8/nSOibDMLR582ZdddVVqq2tVTAY1HvvvafPf/7z2rx5s6699lp94hOfGNCzzjzzTFVUVGjDhsTKa0PxxhtvSJIuvfTSlDwvFQjsQAq5nXZd9YGpumTeBPX3HcgDxzr04KodKjCTl2jst4GSn8AOABi4W265RVdeeaVWrFihmTNnasqUKbrrrrtS+o7Vq1fLMAwZhtG9Hn7p0qUyDCPpJs/7779fjzzyiFpaWnTZZZepsLBQCxYs0P79+/XYY4/pkUceGfC7bTabbrjhBtXV1Wnz5s3D+nkEg0E9/fTTWrJkiWUn1HRg0ymQYoZhaMmc8aosztfj6/eqPZx813p7OKpn34pJZuJvw1A0pPdsMc1NdiMz7ACAQfD5fN1Ng0bKkiVLBrVZtKioSNdee62uvfbalLz/a1/7mh555BHdeeedevLJJ4f8nJ/97Gc6fPiw/vznP6dkXKnCDDswQmZP8OnmZbM0scjdz1U22WKFag9H1fePudpwc/JbCOwAAPTi9Xr11FNP6bnnntM999wzpGe8+OKLuv322/Xggw9q/vz5KR7h8BDYgRFUWuDSDYtn6swpxZbXuG0lCkdjCvaZia8N7E9+A5ViAABIMH/+fL3yyit67LHHVFdXN+j7b7nlFq1cuVI33HDDCIxueFgSA4wwl8OmTy2YrMqSfP2/zQcV6zOVnmcUSzqkcNSU23n8eG3Tu1L+yVJ7U+JDW+ulkuTr3wEAGKtOOeUUbdq0SeFwYsPCE1m/fr08Hs8IjGr4mGEHRoFhGDp/Vrmuu2C6vHn2XufcRnz23ZTZa/1ffVu9GvItmjawLAYAgKRsNpvy8qzLLFvJ1LAuEdiBUTVjnFc3L52twvzj39yyGU4V2CokKWH2vdaIJH8QlWIAABgzCOzAKCvyODW5OL/3Mdt0SVKszw772khL8ocwww4AQNoNpjLOcBDYgTQoLej9rbpie7z9cd8Z9i3tFsGcwA4A/TI6m2HEYrE0jwS5rCuwG/01X0kBAjuQBqUFrl5fF9mTz7C/27pHYTPJXzZUigGAfnWtYW5ra0vzSJDLun59DWXN/GAQ2IE0KPP2DuwFRoUcRn5CYA/Fwtrp7L1JtRuz7ABgyefzSZLq6+vV2tqqWCw2assXkNtM01QsFlNra6vq6+N/F3f9ehsplHUE0qDvDLth2FRkO0mNsa0J19baTM1J9hBKOwKApdLSUrW1tSkQCGjfvn3pHg5ymMfjUWlp6Yi+gxl2IA2K853qu9ytyD5dpmkmdDzdEvUnfwiVYgDAks1m05QpUzRu3Di53e4RX2OMscUwDLndbo0bN05TpkyRzTaykZoZdiANHHabivOdagocb+zQs1KMvcdfLLUdRySnN/EhLIkBgH7ZbDaVl5ervLw83UMBhoUZdiBNLDee9ikVUx86poZYKPEBBHYAAMYEAjuQJn03njoNjwpsFUrYE2WzqTbSmvgAKsUAADAmENiBNOlbi12KL4vpWylGMqw7njLLDgBAziOwA2lS1mdJjBRvoNS3eZIkbTEDyR9CYAcAIOcR2IE06buGXYqvY0+cYZfeDTUlb6BEpRgAAHIegR1Ik2SBvcCokE3uhOMhmdoZTdKtjxl2AAByHoEdSBO3064CV+8upvEGStMSZ9kNu2qjSTaeth4cwRECAIBMQGAH0qjUm2xZTJJ17DabtiStFNMkhTtGZnAAACAjENiBNEq28dSyUkzMYuOp/1DqBwYAADIGgR1IoxLPwDee1ptBGigBADAGEdiBNOrbPEmKN1AqckxKvNiwJ2+gRKUYAAByGoEdSKNkzZMkqdg2PfGgYVNttCXxODPsAADkNAI7kEbJSjtKUr6mJR60mmGnUgwAADmNwA6kUaHbIbthJBwvMKfJ7LuO3WbTtqg/sYESlWIAAMhpBHYgjQzDUH6fWuySVGCbqDy7p+/VCknJGyhRKQYAgJxFYAfSLN+ZGNgNw6bJBbMSL7bZLBoosY4dAIBcRWAH0izZDLskTXAlCeyGPXkDJdaxAwCQswjsQJolm2GX4vXYExg2i9KOLIkBACBXEdiBNPNYzLC7YlMTDxp21cc6EhsoMcMOAEDOIrADaea2mGFva3doamGf0G6L/5at6zvLTqUYAAByFoEdSLN8p11JKjvKH4xqTnFVn6OGZNi0JVkDJZbFAACQkwjsQJrZbIaK851Jz03xnpzsBosGSlSKAQAgFxHYgQxg1fF0nGtm4kHDnryBEuvYAQDISQR2IAOUeZMH9jxzggqcBb0PGjaFzFhiAyVm2AEAyEkEdiADlBbkJT3eHIhobunc3geN+CbVhAZKfgI7AAC5iMAOZIAyiyUxDW0hzSub1/tgZ6WYhAZKVIoBACAnEdiBDGC1hr2xLajqsuo+R+OVYhJKO0rMsgMAkIMI7EAGsArszYGwTvRJa4kAACAASURBVC6Zm3jCZtPBWIcaExooUdoRAIBcQ2AHMoDbaVdBko6nMVOKRPI0xTel94mudex9Z9mpFAMAQM4hsAMZotSiUkxTWyhxWYzRuY69bwMlKsUAAJBzCOxAhij19LPxtLzPxlNm2AEAGDMI7ECGsN54mmSGvbNSTEIDpY5mKsUAAJBjCOxAhrBqntTQFtJJhSf1aaBkWDdQolIMAAA5hcAOZAir5kmN/pBshi2xgZLNooES69gBAMgpBHYgQ1gtiWkKhGSaZmIDJcOigRKBHQCAnEJgBzJEodshp91IOB6MxOQPRiwrxSQ0UCKwAwCQUwjsQIYwDKPfjadVZVV9bogviUlooESlGAAAcgqBHcggZRaBvaEtJJ/L17uBku34b99e5R2pFAMAQE4hsAMZpL+Np5L6LIsxrBsoUSkGAICcQWAHMkh/S2IkJTZQslk1UCKwAwCQKwjsQAbprxa71HeGXd0z7AkNlFjHDgBAziCwAxmkxGM1wx6UpMQGSp2BPd5AKXD8eOuhERsjAAAYXQR2IIOUeJwyEis7yh+MqiMcTWyg1FkpRpJqe65jZ4YdAICcQWAHMojDblNxvjPpuaZAkmUxPSrFbKFSDAAAOYnADmQYq42nDSeoFJPYQIlZdgAAcgGBHcgwVhtPG602ntosGij5WccOAEAuILADGcayFntnYE9ooGRYNFBihh0AgJxAYAcyTH/dTrv0mmXvEdh7NVCiUgwAADmBwA5kGOvmScHuH/dqoNSjUkxdxH/8ODPsAADkBAI7kGGsAntzIKxozJRkXSlma7T1eAMlKsUAAJATCOxAhnE77Spw2ROOx8zjpR17N1Ay+mmgxCw7AADZjsAOZKBSi0oxTZ3r2G2GTXNK5xw/YbNooESlGAAAsh6BHchApZ4TbzydV9ZzHbtFAyVm2AEAyHoEdiADWW88PXGlmF4NlKgUAwBA1iOwAxnIqnmSdWnH40tiejVQYoYdAICsR2AHMpBl8yT/8cDeq4GSrfdv5e5ZdirFAACQ9QjsQAayWhLTFAjJNM3ur4/Psht9Giixjh0AgFxBYAcyUKHbIafdSDgejMTkD0a6v+7VQKlnpZie69ipFAMAQFYjsAMZyDCMgW08LU2+8bRXAyVm2AEAyGoEdiBDlVkE9p4bT6cVTpPH6Yl/0SOw92qgRKUYAACyGoEdyFAD2Xhqt9k1t3Ru/Aujd3fU7gZKzLADAJDVCOxAhhrIkhipRwOlPpVitvSqFNOe8vEBAIDRQWAHMpRVYG/oE9itKsX0bqBUn+rhAQCAUUJgBzKU9Qx7sNfXvRoo2Xo3UGrqaqBEpRgAALIWgR3IUCUep4zEyo7yB6PqCEe7v+7VQMno/Vu6u7wj69gBAMhaBHYgQznsNhXnO5OeawpYLIvpE9i7GyhRKQYAgKxFYAcymOU6dr9VYO9TKYYZdgAAsh6BHchgZd7hVYrpbqBEpRgAALIWgR3IYJa12PsE9uMNlIx+GihRKQYAgGxEYAcy2EC6nUp9GijZLBooUSkGAICsRGAHMthASztKPZbFUCkGAICcQmAHMphVYG8OhBWNmb2OWVWKOR7YWRIDAEA2IrADGczttKvAZU84HjMTSztWlVXFf9CnUkx3AyUCOwAAWYnADmS4EotZ9qY+69gLXYXxBkq2xN/WtZFWKsUAAJClCOxAhhvoxlOpa1mM0U8DJWbZAQDINgR2IMNZbzy1CuxKrBTDOnYAALIWgR3IcFbNk5LNsFtViuluoOQnsAMAkG0I7ECGs2ye5E8M7N0NlPoE9pAZ065ogBl2AACyEIEdyHBWS2KaAiGZZu/Sjt0NlIzEyjJboi3UYgcAIAsR2IEMV+h2yGk3Eo4HIzH5g5GE4/PK5vVTKeYYlWIAAMgyBHYgwxmGMYSNp4mVYth4CgBAdiKwA1lgMKUduxso2WigBABALiCwA1lgMBtPuxsoGRbLYqgUAwBAViGwA1lgMEtipM5lMUkC+5ZoKzPsAABkGQI7kAWsAnuyJTFSV2BPrBRTG2mlUgwAAFmGwA5kAesZ9mDS41aVYrZGWxVpb5JCgZSODwAAjBwCO5AFSjxOGYmVHeUPRtURjiYcjzdQKkjaQGlnNCD5D43UUAEAQIoR2IEs4LDbVJzvTHquKZC4LKa7gZLNqoES69gBAMgWBHYgS1iuY09SKUay3njKOnYAALILgR3IEmXe1FSKiZd2ZEkMAADZgsAOZAnLWuyDrBRzMNahpubdqRwaAAAYQQR2IEsMptup1NlAqXBq0nO1bfupFAMAQJYgsANZYrClHSWpuvyU5MtionQ8BQAgWxDYgSxhFdibA2FFY2bSc9Vl1UkrxcQ3nrKOHQCAbJBRgd0wjELDMH5kGMZuwzBChmGYhmH8c7rHBWQCt9OuAldi+I6ZyUs7Sp0NlJLMsG+NtirSsi/lYwQAAKmX8sBuGMa/dgZt0zCMOYO8/TFJt0jaIukeSd+U9KdUjxHIViUWs+xNFuvYpxVOk8fhSTgeNGPa2bA1pWMDAAAjI6WB3TAMQ9J1ko51Hrp+EPfOlXSZpGdN0/yIaZp3mKZ5p2madakcI5DNBrvx1G6za07J7KTnthzbkbJxAQCAkZPqGfaLJU2X9ENJmyVdYxhG8oSRaFnnf59M8ZiAnGG98TR5YJekeePPSnq8tv0QlWIAAMgCqQ7sXTPqj0paKalc0sf7u8EwjE8ahmFKeqjz0M96LKmZm+LxAVnNqnmS1Qy7JFWPP7WfBkpUigEAINOlLLAbhjFB0kclrTVN8z3F16NHJX3pBLceVHyt+hFJkc4ff1PSnZK2p2p8QC6wbJ7k7yewW1SKORjrUFMDy2IAAMh0qZxh/4Ikp+Iz6zJNs17SXyUtNQxjltVNpmm+IunbkryS6jrXrd9pmuY3TdOMpnB8QNazWhLTFAjJNJOXdix0FWqKuzzpubojb6ZsbAAAYGSkJLB3bjb9oqR2Sf/d49RKSV3n+lMtKV/SxlSMB8hVhW6HnHYj4XgwEpM/GLG8r7okecGmLY3bUjY2AAAwMlI1w75M0kxJT5mm2dLj+NOSmiWtMAzD2c/9XbviCOxAPwzDGNLG0+rxpyc9XttGLXYAADJdqgJ71zr1lT0PmqbZIel3kiZIuqKf+7sC+6YUjQfIWYMt7ShJ8yZ+IOnxraFGRTpakp4DAACZYdiB3TCMcZI+Jmm/pOeTXNIV4vvbfHqWJFMSC2qBExjKxtNpJbPksScG/aAZ0876DSkbGwAASL1UzLBfI8kl6Vemacb6nuzcVLpd0oWGYUzve75z/fvpkrabptmagvEAOa2kIPnqsv6WxNhtds3Jr0h6bkv9GykZFwAAGBmpCOxdG0pX9nPNr2S9+fRkST6xHAYYkDKLGfb+lsRI0rzi5MWaahu3DntMAABg5DiGc7NhGEskzVG8Osw/xSfLk5rQ+d8vGIbx76Zp9ixnwYZTYBCsN50G+72vuvxUad+qhON1re+nZFwAAGBkDCuw63hn03xJ1w3g+omSLpf0VI9jBHZgEEo8ThmG1Lfsuj8YVUc4KrczsUmSJFVPXJh0l8iB0DE1dTSpxF0yAqMFAADDNawlMaZpXmWapjHIz1N9nvH1zuPJNqwC6MNht6k4P/k69qaA9bKYwtKZmmLPTzxhxlR3iBVpAABkqlR2OgUwSqyWxTT0UylGdqeq3eOTntpS/3oqhgUAAEYAgR3IQmXewTdPkqTqwpOSHq89umW4QwIAACOEwA5kIcta7CeqFFN2StLjW1t2KxKLJD0HAADSi8AOZKGhdDuVpGnjTpHHSNyUGowGtfPYzpSMDQAApBaBHchCQy3taC+cpDkOX+IJM6bahtpUDA0AAKQYgR3IQlaBvTkQVjRmJj0nSfJO0Dx7YeJxM6baw2+naHQAACCVCOxAFnI77fK4Epe2xEypuZ/SjrI7Ve2dnPRU7VECOwAAmYjADmQp62Ux/a9jryo5OenxA231aupoGva4AABAahHYgSw11I2nRcXTNdmqgVJDXSqGBgAAUojADmSpoc6wy1ehefYkG09jUW1poB47AACZhsAOZCmr5kknmmGXr0LVVIoBACBrENiBLGXZPMl/gsDunaB5jqLE42ZMWxvraKAEAECGIbADWcpqSUxTICTT7Ke0o92pab4pyRsohQPadWxXqoYIAABSgMAOZKlCt0NOu5FwPBiJyR/sf5bc7pto2UCJdewAAGQWAjuQpQzDGMbG04nJN56yjh0AgIxDYAey2FBLO8pXoapkM+yxKIEdAIAMQ2AHstiQN576KlRtMcN+wH+ABkoAAGQQAjuQxUoKnEmPn3BJjHeCimyuxAZKZkySSQMlAAAyCIEdyGJlFjPsJ1wSY3dKBeU0UAIAIAsQ2IEsZr3pNHjim70TLBso1TUyww4AQKYgsANZrMTjlJFY2VH+YFQd4Wj/N/smap6jMPG4GVVdAw2UAADIFAR2IIs57DYV5ydfx94UOPHG02k2T2IDpVhMwWiQBkoAAGQIAjuQ5ayWxTQMoFKM3TASGyiZMUliHTsAABmCwA5kuTLvEJsneSdIMhLLO3ZWiqEeOwAAmYHADmQ5y1rsA6wUk3TjKQ2UAADIGAR2IMsNudupFK8UQwMlAAAyGoEdyHLDKu3om6gimzNJA6V4hRnKOwIAkH4EdiDLWQX25kBY0ZjZ/82+iZKU2EApFt94yrIYAADSj8AOZDm30y6Py55wPGZKzScs7ThBkhLXsZsEdgAAMgWBHcgB1stiBlYpJqGBUmelGBooAQCQfgR2IAcMeeNpZ6WYaTaP8hMaKEVpoAQAQAYgsAM5YMgz7JLknSC7YWguDZQAAMhIBHYgB1g1TxpQacfOjaeJDZTilWJYxw4AQHoR2IEcYNk8yT+IwN53hp1KMQAAZAQCO5ADrJbENAVCMs0TlXbsrBRjMcN+wH9AzR3Nwx4jAAAYGgI7kAMK3Q457UbC8WAkJn/wBFVeOivFJDRQMk1J8bBf28gsOwAA6UJgB3KAYRhD33jaWSlGStZAiXXsAACkG4EdyBFDLu0oSb4KSTRQAgAgExHYgRxRYjXDPpCNp96uwN63gVJ8hp0GSgAApA+BHcgRw6rF3lkp5qS+DZQ6K8XQQAkAgPQhsAM5osyitONglsQkNFDqnGGXaKAEAEC6ENiBHGE9wx488c3e8ZLiVWZ6lXfsUSmmrqFumCMEAABDQWAHckSJxykjsbKj/MGoOsLRxBM99agUk9hAKX4vM+wAAKQHgR3IEQ67TcX5zqTnmgKDqBST0EApvo6dBkoAAKQHgR3IIVbLYhoGUimmc+NpYgOl47PzNFACAGD0EdiBHFLmHUalmM7SjlKfWfbOSjES9dgBAEgHAjuQQ0otKsUMrLRjj8BuUSmGwA4AwOgjsAM5ZFjdTntUipnXs4FSn0oxNFACAGB0EdiBHDKs0o49KsUkNlCKz7IHo0HtPrZ7uMMEAACDQGAHcohVYG8OhBWNmSd+gGUDpePr2CnvCADA6CKwAznE7bTL47InHI+ZUvOASjtO7P5h7wZKrGMHACBdCOxAjrFeFjOMjacxZtgBAEgXAjuQY4a38dSitGOPGXYaKAEAMLoI7ECOGdYMe49KMUU2pyq7Gij1qBQj0UAJAIDRRGAHcoxV86QBzbDbnVLBuO4v5/VqoMQ6dgAA0oHADuQYy+ZJ/gEEdknyTej+YbVFpRgCOwAAo8eR7gEASC2rJTFNgZBM05RhGP0/wDdRqt8sqW8DpeMz7G8deUuf+vOnhj3WkWLGTLXua5U76NZFVRfpxktulN2WWD0HAIBsQGAHckyh2yGn3VA42rvuejASkz8Ykc/t7P8BPSrFdDVQajejvSrFmKaphvaGlI47VcyYqcOvHVbL9hZFAhFtfGGjXtrwkn51+6+U50j+3QcAADIZS2KAHGMYxjA3nh4P7L0aKPWYYc9kgQMBtWxvkX+vX7FwTP69fr362qu69tFrqW4DAMhKBHYgBw2vtOPxSjGSdErXxtM+lWIyVbg1rEggIlehS8HGoFyFLkUCEdXtrdPNq27W3pa96R4iAACDQmAHclCJ1Qz7QDae9qkUc2neBNm71r3HMn+W3elzyuFxKNQSUrg1rFBLSA6PQ06fUwf8B3TTqpv05uE30z1MAAAGjMAO5KBhLYmReq1jr7C59Z2Cak2w5fWqFJOpPJUeFc4ulHeKV4bdkHeKV4WzC+Wp9EiS/CG/vvHSN/Tc+8+leaQAAAwMm06BHFRmUdpxQEtipHhgr3+7+8uFzhI9XnS2mifPV2Tuh1MxxBEV+UhE33762/rDz/+gScsnyVPp6VUdJxKL6O6/3a0D/gO6uvrqE1fOAQAgjQjsQA6ynmEPDuwBPWbYeyruaJXyy4c6rFH1wJUP6P217yswOWB5zcotK3XAf0D/tOCf5LSfoHoOAABpwpIYIAeVeJxKNmnsD0bVER7AOnRv8sCu1oPDG9goshk2LahYoFvn3yqbYf1H3XPvP6dvvPQNtYRaRnF0AAAMHIEdyEEOu03F+clnjJsCg68U0y3YKoXahje4UXb5zMt196K75XF6LK9568hbuuWFW7Tfv38URwYAwMAQ2IEcZbUspmEIlWJ6aa0fxqjS4+yKs/WjpT/SOI/Fz0nS3ta9uvmFm7Xl6JZRHBkAACdGYAdyVJk3dZViesnCwC5JM4pn6KHlD2l2yWzLa44Fj+kfV/+jXtzz4iiODACA/hHYgRxValEpZviBPXvWsfdVnl+u+5fer3MnnWt5TTgW1rdf+7Z+U/cbmWbmN4oCAOQ+AjuQo4bV7VSyDuz+Q0McUWbId+TrW+d9S5+Y/Yl+r/vF5l/oPzf8p8Kx8CiNDACA5AjsQI4admnHHKgUY8Vus+vmM2/WLWfe0m8N9v/d9b+6fe3t8of8ozg6AAB6I7ADOcoqsDcHworGBrDUwztBuVIpxsrHZ39c3z7/23I73JbXbDy0UbesukX1bdm5dh8AkP0I7ECOcjvt8rjsCcdjptQ8kNKOdkdOVYqxct6k83T/0vtVll9mec37Le/rphdu0tbGraM4MgAA4gjsQA6zXhYzNivFWDm55GQ9tPwhzSiaYXlNU0eTvvriV7V239pRHBkAAAR2IKeN2MbTHFjH3td4z3j9cNkPdXbF2ZbXhKIh3fnKnfrdtt9RQQYAMGoI7EAOY4Z9cAqcBfrOBd/R5TMvt7zGlKmH33pYP9r0I0VikVEcHQBgrCKwAznMqnnSgGfYrSrF+HMzsEuSw+bQV8/6qm44/QYZyTbddnp6x9P6t5f/TYFwYBRHBwAYixzpHgCAkWPZPMk/0MA+QTJskhnrfTzYKr3wbamfkogZYe96af8bUuX8Qd1mGIY+PefTqiio0F1/u0uhaPL/X387+Df9w4v/oLsvuFvjPBYbdAEAGCYCO5DDrJbENAVCMk2z3xrkkuKVYjzlUtvhxHOBoykY4QgLNEgbH5Xam6RZFw769g9O/qDG5Y/Tv778r2ruaE56zc7mnfryC1/W3RfcrVkls4Y7YgAAErAkBshhhW6HnPbEUB6MxOQPDnD9tdU69mxS9z/Sodoh3VpVVqUfL/+xphVOs7ymob1B//DiP+jVA68OdYQAAFgisAM5zDCM4W88LaxM4YjSxZQ2/UryHxnS3RUFFXpg2QM6c/yZltd0RDp0x8t36KntTw11kAAAJEVgB3KcVWAf8MbTyWcracfTbBMOSBsekSLBId3udXn13Q9+V5ecdInlNaZp6oFND+jHb/5Y0Vh0qCMFAKAXAjuQ4yxn2Ae68bSgTFpwreTypnBUadJ6UHrrcWmINdSdNqe+cfY3dN2p1/V73e/f/b3+/ZV/V3ukfUjvAQCgJzadAjlu2EtiJGniaVLFqfHNm9kwc+w/FJ9NT+bAJqloijRr+ZAebRiGrqq6ShMLJup767+ncCyc9LpXDryiW1ffqu+c/x2V5ZcN6V0AAEgEdiDnlVmUdhzwkpguhiF5SlMwolHgHSfN+4SkV5Kfr/uzVDRZGjdnyK9YNnWZxnvG6451d6gl1JL0mncb39VNL9ykuxbdpRlFM4b8LgDA2MaSGCDHWc+wD20td9Y46YJ4KE/KlN5YKbU1DOsVp5SfoocufEiVXuuNuYcDh/WVVV/R6/WvD+tdAICxi8AO5LgSjzNpfyN/MKqOcBYsbxkqw5DGV8eXvyQTbuvchDrI7zT0Uemt1IPLH9Sp5adaXtMWbtPta2/XX3b+ZVjvAgCMTQR2IMc57DYV5zuTnmsKDC+sZjybXTr7OusNsy37pbefGPIm1C5FeUX6z8X/qeVTrdfFx8yYfrDhB/r52z9XrG/nWAAA+kFgB8YAy9KOA60Uk83yS6T5X5AMiz/u9m+Qdq0Z9mucdqf+5QP/omvmXdPvdY9vfVzffu3bCkZzfEkSACBlCOzAGFDmTUGlmGxWPkuqvsL6fO3T0tHtw36NYRi6Zt41+ueF/yyHzXpP/5q9a/S11V9Tc0fzsN8JAMh9BHZgDCi1qBQzZgK7JE1fLFUuSH7OjElv1EiBxpS86pKTLtE9H7xHBc4Cy2tqG2p106qbtKdlT0reCQDIXQR2YAwoG26301xgGNJpn5EKLSrHhPzShv+Sosnrqg/WGePP0EPLH9LEgomW1xz0H9RNL9ykNw+/mZJ3AgByE4EdGANKxmppx74crvgmVKuZ72N7pbd/N+xNqF2mFk7VQ8sfUnVZteU1beE2feOlb+jZ3c+m5J0AgNxDYAfGAKsZ9uZAWNFYasJp1vCUSvOvkZSk1qUk7Vsv7V6XstcVu4v1gyU/0OIpiy2vicQi+t7676nmnRpFYpGUvRsAkBsI7MAY4Hba5XHZE47HTKk510s7JjNujlR1ufX5LX+QGt5L2evy7Hn6t3P+TZ+b+7mEc2bMVNu+NjXXNesnf/2JPv3nT+tHG3+kLQ1bZKZoph8AkN2syxgAyCmlBS4FQu0JxxvbQirzJt+UmtNmLosvgTmwKfGcGYuvZ//gP8XLQqaAzbDp+tOu10TvRN3/xv2KmTGZMVOHXzuslu0tigQicngc8u/xq+ncJv1xxx81sWCilk5dqmVTl2lG0YyUjAMAkH2YYQfGCDae9mEY0umfk3yTkp/v3oSa2iUql824TN9d9F0VOAsUOBBQy/YW+ff6FQvH5N/rV8v2FgX2ByRJB9sO6jd1v9EXn/2irnv2Oj1W95gO+g+mdDwAgMxHYAfGCKvmSWOqtGNfjrzOTaie5Oeb90jv/D7lr11QsUA/XPZDecNeRQIRuQpdCjYG5Sp0KRKIKNyaWKlm17FdemTzI7rq/12lm1+4WU9tf0qNHakpQwkAyGwEdmCMsGqeNGZn2LsUlEtnXS3LTah7XpXefyXlr51RNEPfuuhbGl86XqGWkMKtYYVaQnJ4HHL6nP3eW9tQqwc2PaBP/flT+vqar+uZXc/IH/KnfIwAgMxAYAfGCMvmSf4xHtglaXyVNPcj1uc3/15q3JXy15535nm67f/cpqVnL5UnzyPvFK8KZxfKU2kx49+HaZp649Abuuf1e/TJP31S//fl/6vVe1crGB1j5ToBIMex6RQYI6yWxDQFQjJNU4ZhMcM8Vsy6ML4J9eBbiefM6PFNqO6ilL3SMAx99rOf1emnn67vx76vL97yRR0oOKBVe1dpW+O2QT0rHAtr3f51Wrd/nTxOj86fdL6WT12uMyecKaet/xl7AEBmI7ADY0Sh2yGn3VA42rtUYDASkz8Ykc89xkOdYUinXym1HpL89Ynngy3Shl9K594s2VP3R6dhGKqurta0adN0/lnnS5I+NedT2u/fr1V7VumFPS9oT8ueQT0zEA7oufef03PvP6eivCItnrxYy6cu17zyebIZfGMVALINf3IDY4RhGGw8PRGnO74J1eFOfr5pl7TlqVEZSqW3Up+v/rx+eckv9fOLf67PzPmMxnvGD/o5x4LH9Kf3/qSvvPgVfe4vn9NP3/qpdjTtoMY7AGQRZtiBMaS0wKVDLYnrmxvaQppWVpCGEWUg73jpzM9Lr/88+fn310nFU6Sp54zKcAzD0MzimZpZPFPXn3a9thzdolV7V2n13tU6Fjw2qGcdCRzRE9ue0BPbntAU3xQtn7pcy6ctV6W3coRGDwBIBQI7MIZYzrCz8bS3ilOkky+V3v3f5Oc3/7fkmyiVTBvVYdkMm04dd6pOHXeqvnzGl7Xp0Cat2rtKa/etVXsksSlWf/a27lXNlhrVbKnRnNI5WjZ1mZZOWary/PIRGj0AYKgI7MAYwpKYQTj5kvgm1EPvJJ6LRaQNj0iL/klyF47+2CQ5bU4tnLhQCycu1K3zb9VrB17Tqr2r9NqB1xSOJdZx78+2xm3a1rhND7/5sE4ff7qWT12uD07+oHwu3wiNHgAwGAR2YAwpsyjtOOZrsSdjGNKZfyetvVdqO5x4vuOY9EaNdO5Nks0+6sPrKc+ep8VTFmvxlMXyh/xat3+dXtjzgjYe3jioteqmTL15+E29efhN/XDjD3V2xdlaPnW5zp10rvId+SP4MwAA9IfADowh1jPs1O1Oypkf34S69l4pWW3zxvek2j9Kp3xy9Mdmwevy6kPTP6QPTf+QGjsatWbvGr2w5wXVNtQO6jmRWESvHnhVrx54VW6HW+dNOk/Lpi7T2RVnUyYSAEYZgR0YQ0o8ThmG1HfS1R+MqiMclduZ3pnijOSrkM68Kl6HPZldL0lFU6UpZ4/uuAag1F2qj8/+uD4+++M66D+oVXtX6cU9L2rnsZ2Dek5HpEOr9qzSqj2r5HP5tHjyYp1feb7K88tV4CxQgbNAHodH9jR/pwEAchWBHRhDHHabivOdagokrnFuCoQ0sYhlD0lNPF2afbG0/a/Jz7/9RDzYF08Z3XENwkTvRF1VdZWuqrpKO4/t7A7g9W1Jas73o6WjRU+89IR+3fprOX1OeSo93U23OV15PAAAIABJREFUPE6PCpwF8jq9vf/r8iY9XuDqfSzPnkcDLwBIgsAOjDGlBa6kgb3BT2Dv18mXSsf2SYeTLC2JhY9vQs3zjv7YBmlG0QzNOHWGrjvlOtU21mrVnniZyKaOpn7vM2OmDr92WC3bWxQJROTwOFQ4u1Djzx0vwzAUCAcUCAd0REeGNC6HzdE9Y98z7HscHvlcvqT/COj1DwBngRw2/loDkHv4kw0YY8q8Lr13pC3hOJViTsBmi9dnX3ev1JYkkLY3SRtXSh+4MX5tFjAMQ/PK5mle2TzdePqNeuvIW3r+/ee1bv86tYUTf40EDgTUsr1F/r1+uQpd8u/1S5K8U70qmDz8Ov6RWETHgscGXV++J7fDfTzA2wvUUd8h02+qpLREU2dPlcfpkdvultsR/+Tb87t/3PV1niNPbrtb+Y585dnzWOoDIO0I7MAYU2pRKYbAPgAuj7TgOmndfck3oR59V6r7kzTvY6M/tmFy2ByaP2G+5k+Yr1ujt2p9/Xq9sOcFvXrgVYWi8V8b4dawIoGIXIUuBRuDyivNUyQQUbh1cGUkR1JHpEMdkQ4daTvS73cDBsNpcx4P9Z1BPunX9uPH8hx5if8g6HNvvj1fDpuDZUAATojADowxZRaVYijtOECFE6UzPhcv6ZjMzhfja9kr54/qsFLJZXfpgsoLdEHlBQqEA1q3f128u+r+1XJ4HPLv9SvcGpbhMOSd4pXTl3lVY1L53YBwLKxwKKzWUGvKx2kzbN0hPs8eD/ml7lLNKZmjqrIqVZVWqcRdkvL3AsguBHZgjCmhtOPwTTpTat4rvfdC8vNvPi55K6SiytEd1wjwOD26+KSLdfFJF6tpQZPust2lda+u0/YN21Uxq0K+WT6VnlQ66E6rIy0bvhsgSTEzprZwW68lSDu1UxvqN3R/XVFQobmlc1VdVq2qsirNLp4tlz3572MAuYnADowxVjPszYGwojFTdhvfnh+QuZdJLfulI1sTz3VvQv2a5Br+2u5MUZJfonv+4R7VXVSn73//+/r617+uqqoqGYahaCyqQCSgtnCb/CG//GF//Mdd/w31+TrceU3o+LFILJKysTp9zqz5bsCJ1LfVq76tXqv3rpYUX740o3iGqkurNbd0rqrKqjTZO5mlNUAOI7ADY4zbaZfHZVcgFO11PGZKzYGQyrzJ17ijD5tNOutqae0PpEBD4vlAg7TxUWnh32fNJtSBMAxD1dXVmjZtmqqrq7uP2212+Vw++Vw+aYj/RglFQ2oNtSoQDqg13Ho86EeOB/7+/hHQc5baU+lR4exCSVJTS5O8U7wqnF0oT6VnWD//TBCJRfRu47t6t/Hd7mNel1dzS+dqXtk8zS2dq7mlc1WUV5TGUQJIJQI7MAaVFrgUCCUuYWhsI7APiqvg+CbUWJKlFke2Stv+IlVdPvpjy0Iuu0tl+WUqyy8b0v0xM6ZAOCB/2K9AOKCWpS3aUrtFjz70qD563UdVflK5gtGgOqLxjald/w1Gg2qPtPc61vV1MNnm4gzkD/m1oX5Dr6U0k7yTVFVa1b2UZmbxTLrUAlmKwA6MQWUFLu1rSgzsDW0hzU7DeLJaUaV0+uekTY8mP7/jealocnzdO0aUzbDJ6/LK6zpeC/+M8Wdo++rtuulDNw3pmTEzFg/5ndVn2qP/v707j6+qvPY//llJTshEQhJkUmQSAQEnRAVHhDpUsWqdSnvVOra3tk51qrao1VbUTtZa69A63Ntatb86YWt7FUQZKzgwBJRJEBBIAoEMZHx+f+ydGJKzSQI5U873bfPacp5nn7PYjck6+6xnPVW7JfnVddW7J/mNbwbC/bnZuY1vDuob6tsOYi9tLN/IxvKNvLXOW2sRSgkxNH9oUxnNiIIR9M3uq1IakQSghF0kCRUELjxVp5i9csAYKFsHq2eGH29chJrbN6phyb5r7OKSmRaZTcVqG2qbEvpddbvYUbuDT7d9yvLS5RSVFLF+5/pOfa1lJctYVrIMPvUey+uWx4iCEU2LWocXDN/tDY+IxAcl7CJJqDBHrR073YivwY6NXi/2luqrvUWox9/o9XIX8YVSQoTSQ17tv29k4cimf99Zs7MpeV9eupyi0qJ92liqpbLqMuZtmse8TfOaHuvfvX9T8j6icASD8gaplEYkxpSwiyShwM2TypWw77Xmi1CrtrUer9gKHzwHR18NKkGQduqe3p2xfcYyts9YAJxzbKrYRFFJEctKl7G8dDmfbvu0UzvsrN+5nvU71/Pm2jcBb23B0PyhXi18gVdK0yur4xtQicjeU8IukoSCSmK2VdbgnNMv4r3Vrbu3CHX2b8IvQt2yDFb8A4Z/NfqxSZdgZvTL6Ue/nH5MHDARgNr6WlZuX8ny0uUsK1lGUWkRG8s3dtpr1tTXsLR4KUuLlzY9VpBR0HQHfly/cQzOG9xprycirSlhF0lCuRlphFKN2nq32+PVdQ2UV9fRPUMff++1Hv3h0Ivgw/8JP/7pm94i1L6HRjcu6bJCqSFvEWnhCM4dei7glboUlRaxvGR505348pryTnvN0l2lzNk4hzkb5/DU4qe4bORlXDLykk57fhHZnRJ2kSRkZuRnpbNlZ+uWdaUVNUrY91X/sbD9M1j7bvjxD/8XcnpD997RjUuSRl63PI7teyzH9j0W8EppPi//vKkWflnpMlZtX9VpXWqeXvo0aSlpTBkxpVOeT0R2p4RdJEkV5oRP2EsqahhQ2HV254yZked6i1BLV7Ueq9sF/3nS2wk1lBH92CTpmBn9u/enf/f+nDrwVMArdfl0+6deGY2fyH9R8cVev8aTi58kJz2Hs4ec3Vlhi4hPCbtIkgps7aiFp50jJRXGXAbvPgS7wnT1qNjiLUIde6UWoUpMpKemM7Jw5G5daUp3lTZ1pSkqLWJF6YrddpBty28W/obsUDYTD5wYiZBFkpYSdpEkpV7sUZCR6y1CnfMwhOvisXkJfPovOPi06McmEkZBRgHj+41nfL/xgLdx1Pqd65u60hSVFLGmbA0NriHs+Q7H/QvuJzMts+k5RGTfKWEXSVKFAa0d1Yu9k+UPgNEXwEd/CT++4h+Qu390YxJppxRLYUDuAAbkDuD0QacDUFVXxfLS5fx8/s8pripudU59Qz33zL2H+0+4n8N7HR7tkEW6pJRYByAisRF8h711XbvsowOPhQHHBQw6rzSmpv1lByKxlJmWyRG9juDBkx4kr1te2Dk19TXc8d4dLC9dHuXoRLomJewiSSo/KxS2dLq8up5dtZ3TOUKaGXke5A8KP1a3CzYsgtpd0Y1JZB8MyB3AtBOnkR0Kv0i9qq6KW2fdypqyNVGOTKTrUcIukqTSUlPokRm+feO2SpXFdLrUNDjq29AtN/x4TTl89GdwLvy4SBw6OP9g7jv+PtJTw39it7NmJ7fMuqVTN3ISSUZK2EWSWFBZTIk6xURGRp6XtFtq+PFNH8GSv0Gdrr8kjkP3O5S7x99Nakr47+uSqhJunnVz2Hp3EWkfJewiSawwR51ioq5gMIz6evD42ndhxn1eiYzutkuCOKbvMfzo6B9hhG9Ruql8E7fMuoWy6jAtTkWkTUrYRZJYQUCnGCXsETZgPPQ/Nnh813ZY9AzMfQTKNkQvLpF9MOHACdxw1A2B42vL1nL7u7dTWVsZxahEugYl7CJJrCAroCRGCXtkmcHo86HHgXueV7ISZj0Ii19SFxlJCGcNPotrDr0mcHx56XLunH0nNfX6GSPSEUrYRZJYQWBJjFo7RlxqCI66HDJ6tDHReWUyb98Ha2dDQ/gNa0TixUXDL+KbI74ZOP7hlg+5e+7d1DbURjEqkcSmhF0kiRUGLDrdXllLfYPqpyMuMx+OvwF6j2p7bm0FLH4B3n0ISlZFPjaRfXD5qMs556BzAsfnbpzLAwseCNwxVUR2p4RdJIllhFLJSm/d2aHBwXa1doyOzB5w9FVwwFGQ3avt+Ts2wJyHYeEzULUt8vGJ7AUz49ojrmXSgEmBc95a9xa//eC3OC2uFmmTEnaRJBe846kS9qjK3g9OuhUOOQfSMtqev3ERzPgZfPpvqFdpgcSfFEvh5rE3M67fuMA5r6x8haeWPBXFqEQSkxJ2kSQXVBajhacxkJoGQybAhDug/zFtz6+vgeWvw8yfwxeL1QZS4k4oJcTUcVM5vNfhgXP+XPRnnl/+fBSjEkk8SthFkpzusMehjFw4fIpX395WJxmAyhL4z5Mw/w+wc3Pk4xPpgPTUdO497l6GFwwPnPP4x4/z+urXoxiVSGJRwi6S5II2T9Id9jiQPxCOvxEOmwLpOW3P31oE79wPS/8OtVURD0+kvbJCWdx/4v0MyhsUOOdX7/+KGetmRDEqkcShhF0kyQVunlSuhD0umMGBx8Apd8LgCWBt/Nh2DbB6Jrx9L6ybrzIZiRu56blMO3EafXP6hh13OH624GfM2zQvypGJxD8l7CJJLqgkZltljbo3xJNQJow8B066DfYLLi1oUlMOH/0Z3vslbFsb8fBE2qNnZk8eOvEhCjMLw47XN9Rz15y7+GjrR1GOTCS+KWEXSXK5GWmEUq3V49V1DZRX18UgItmj7r3hmO/A2CshK3zSs5vt6+C9X8GHf4ZdOyIfn0gb+ub05cETHyQ3PTfseE19DXe8dwcrSldEOTKR+KWEXSTJmRn5WVp4mlDMoM9oOPl2GH4WpIb//2836+fDjPtg1dtQrzdiElsD8wYy7cRpZIWywo5X1lZy66xb+WzHZ1GOTCQ+KWEXES08TVSpIRj6FZjwI+h3ZNvz63bBslfgnWmwpSjy8YnswbCCYdx73L2kB7zh3FGzg5vfuZkvKr6IcmQi8UcJu4gEt3bUwtPEkJkPYy6F8T+A3P3bnl+xBeY/BguegIriyMcnEuDwXoczddxUUlNa77gMUFxVzA/f+SHFVfo+leSmhF1E1Iu9qygcAif8EEZfCKHstudvXuJtulT0OtRVRz4+kTDG9RvHbUffhtF6LQ3AxvKN3DrrVnbUaA2GJC8l7CJCYUBrR5XEJKCUFBh4HJxyBww8AQKSoCYNdbDy3159+4aFagMpMTHxwIlcP+b6wPE1ZWu4/d3bqaytjGJUIvFDCbuI7OEOu+66Jqz0bBh9Ppx4MxQe1Pb8XWWw6FmY8zCUfR75+ERamDxkMlcfenXgeFFJET+e/WNq6nUjQZKPEnYRIT8rhIW5EVteXc+u2vroBySdJ29/GHctHHkpZPRoe37papj1EHz8AlSXRz4+kWYuHn4x3xj+jcDxD7Z8wD1z76GuQZ2OJLkoYRcR0lJT6JEZCju2rVJ3sxKeGex/pNdNZuhpkJLWxgkOPpvtlcmseRcaGqISpgjAlaOvZPKQyYHjczbO4cH/PEiD0/elJA8l7CICBJfFlKhTTNeR1g2GfxVO/hH0ObTt+bWVsOQlmPUgFK+MfHwieHtDXHfkdZxy4CmBc/792b955INHtBuzJA0l7CICBPdiV6eYLii7EMZeAcf+N+T0aXv+zo0w97ew8GmoLI14eCIplsKtR9/KuH7jAue8vPJlnl76dPSCEokhJewiAkBBQKcYJexd2H7D4KRbYOS5kJbZ9vyNH8CMn8Enb0KD1jZIZIVSQvxk3E84vNfhgXOeW/YcL6x4IYpRicRGW4WMIpIkCrK022lSSkmFwSfD/mNg+XRYNw/YQ5lBQy2seAPWfOhtvJSW4ZXapGX4X+le8t/0WMtjhtd6UqQduqV2497j7uWmd25iRemKsHMe++gxskPZnDn4zChHJxI9SthFBICCwJIYtXZMCt26w2EXw4DxsORvsG3tnufX7fI2XtobKSEvgQ9lQmq3Zv+e7h3TunmPh/wEv/njTW8M/H8P2CFTuo6sUBb3n3A/18+4ns92fBZ2zi/f/yVZaVlMOHBClKMTiQ4l7CICQGHAotPtlbXUNzhSU9rYgEe6hh4HwnHXw+fvQ9ErUL2z81+joRZqaqGmE9pGpqSFucvvvwlIy/Tu+Jes8r4Kh+z760lM5HXL48GTHuS6t69jU8WmVuMOx88X/JysUBbH9D0mBhGKRJY+lxQRADJCqWSlt75b2eBgu1o7Jhcz6D8WJtwJQyaCxfFd7IY6L/GvLIEdG6B0FWxZ5tXbr5sDq2dC8SfehlAf/C/Uq393ouqZ2ZMHT3qQwszCsON1DXVMnTOVj7d+HOXIRCJPCbuINAne8VQJe1IKZcAhZ8PJt0GvQ2Idzb77fAHMexRqKmIdieylfjn9mHbiNLqndw87XlNfwx3v3cEn2z6JcmQikaWEXUSaBJXFaOFpksvpBcdcA0dfDd37xjqafVO6Ct77FZRvjXUkspcG5w3m/hPuJzOgs1FFbQW3zrqVdTvWRTkykchRDbuINNEddtmj3iO9r/KtMOtuOOpyqKv2FqC2OoZ7rNr72lMXmmio2Ool7WOvUF17ghpROIJ7j7+X22bdRm1DbavxsuoyfvjOD3n4lIfpk92OvQZE4pwSdhFpErR5ku6wy25y9oPsntD3sI6f69wekvz2Jv3NxvY2+a+t8MpjDpsCB4zZu+eQmDqi1xFMHT+Vn8z+CQ2uodV4cVVxU9JekFEQgwhFOo8SdhFpErh5UrkSdukkZl5tfChj35/LOaiv2XNiv3kZMC/8+Q118MGzUFkMQ0/1YpOEMr7feG47+jZ+Pv/nuDBv3jaWb+SWd27hVxN+FVj3LpIIlLCLSJOgkphtlTU45zAlNBJPzL5s4RjkwPGQ//aen2fFG16ZzKEXQ6p+LSaaSQMmUV5bzsOLHg47vrpsNbe/ezsPnPgAWaGsKEcn0jm06FREmuRmpBFKbZ2UV9c1UF6tdniSgFJSoNdwGH0BsIc3nJ//B+b/Xh1kEtQ5B53DFaOvCBxfVrKMqXOmUlvfut5dJBEoYReRJmZGfpYWnkoXNPB4r8tN6h7uxpeshPd+DRXF0YtLOs2U4VO4aNhFgeMLNy/kp/N+Sm19LfUN9TS4BpyL8QJokXbSZ38ispvCnHS27Kxu9XhJRQ0DCrNjEJFIJ+l9CBx3HSx4HHZtDz+nYsuXHWQKBkc3PtknZsbVh15NRW0Fr69+Peyc9za8x2l/O631uRhm/lfjP34JYIqlfDnuf0pjZqSQEnbObs+HNY15/7PdxlrNAXBQtr4MV+EYeeBILp94OQPyBnT25ZIEo4RdRHYT2NpRC0+lK8jbH46/Af7zBJR9Hn5OTTnM/R0cPgX2VweZRGJmXHfkdZTXljNz/cx2n+dw3t32GN9wdw2OLfO2sOPTHdRV1jE/az4vzXqJU792KmcPOZvjDzieUEootkFKTChhF5HdqBe7dHmZPWD8D2DRs7B5Sfg5DXXeeEUJDP2KOsgkkNSUVG4/5naq6qqYv2l+rMPpkMqNlez4dAfl68tJz02nfH05AHM+mMNHWz8ir1seZww6g7MGn0W/nH4xjlaiSTXsIrKbwoDWjurFLl1KWjc46goYdNKe562YDh/9Beq16DqRhFJCTB03lUP3OzTWoXRI7c5a6irrSM9Np7q0mvTcdOoq66jd6S2WLasu4/nlz/OtN77FLe/cwrufvxt24yjpepSwi8hugu+wt65rF0loKSkw6jwY9XX22EFm/XyY/xjUVEYtNNl3GWkZ3Hf8fRycf3CsQ2m3UPcQaVlp1OyooXZnLTU7akjLSiPUvXUZzPub32fqnKlMmT6FPy35E5srNscgYokWJewispv8rFDYT//Lq+vZVVsf/YBEIm3QiXD0VW10kPkUZv/aK5GRhJEdyuahkx7irMFn0SOjh7e4s9ni0XiTtX8WuUNzyemfg6UaOf1zyB2aS9b+wf3jS6pKeG7Zc0x5Ywp3vHcHczfOpb5BP6u7GtWwi8hu0lJT6JEZYltl649Zt1XW0DcvMwZRiURY75Fw3A/8DjJl4eeUb4b3fgljr4SCQdGNT/ZaTnoONx51IzdyY6sx5xwO57V49FecNj7mnPc4fLkotekf59o8N+w5LY4NNDQtdG18noavNLBi+QqmPTCNrElZ1O1X165N65xzzN04l7kb59IrqxdnDj6TMwadQc/Mnp10JSWWlLCLSCsF2elhE/aSciXs0oXlHQDH3wgLnoAde+og8wgc/k3Y/8joxiedrnlbxXgybPwwFh2+iDv+27tj/uqqV1m0eVG7z99SuYU/LfkTzy57luP6HcdZg8/iyN5Hxt3fU9pPCbuItFKYk86qra13fFSnGOnyMnt4d9oXPgNbloaf01AHi56ByhI4aJI6yEjEhFJCnHjAiZx4wIlsKN/A66te5x9r/sGOmh3tOr++oZ5Zn89i1uez6JfTj7MGn8XpA0+nR0aPCEcunU1vtUSkFe12KkktrZtX9jLoxD3PW/46fPQ8qF5YomD/nP255rBreHHyi9xxzB0d7oCzsXwjj3/8OBe+fiH3zruXD7d8qJ1eE4jusItIK2rtKEkvJcXrHpPVE5b+ncAdddbPg6pSGPNtSA9eGCjSWUKpISYOmMjEARNZW7aW11e/zptr36SitvWnouHUNdTx9rq3eXvd2/Tv3p+zh5zNqQNPpXt69whHLvtCd9hFpJWCHLV2FAFg8Ene3fY9dZAp/gRm/0YdZCTqBuYN5NojruXFyS9yy9hbOKTwkA6dv37nen734e+44LULmLZgGktLluque5xSwi4irRQG9GLfXllLfYN+mEuS6TPKq2vPyAueU/6F10Fm29qohSXSKCMtg9MHnc4jEx/hiVOf4OwhZ5OZ1v4GATX1Nby59k2+/9b3uepfV/HKylfafcdeokMJu4i0khFKJSs9tdXjDQ62V6osRpJQ3gFw/A2Qu3/wnJpymPMIbPwwenGJtDCkxxCuH3M9L05+kRvG3MBBPQ7q0Pmry1bzm0W/4YLXLuAX7/+CT7Z9EqFIpSOUsItIWME7niphlySVmQ/jfwC99lB20FALC/8EK/8PVFogMZQVymLykMn84St/4NFJj3LGoDNITw3/cz2cXXW7mL56Ot/593f47v99lzdWv0FVXVUEI5Y9UcIuImEFlcVo4akktVAGjL0KBp6w53lFr8HHL6iDjMScmTG8YDg3j72Zlya/xPeP+D6D8jq28deK0hU89P5DXPjahTy86GFWl62OULQSRF1iRCQs3WEXCdDYQSa7Jyx9mcAOMuvmeL3aj/o2hLThmMReTnoO5w49l3MOOoelJUt5bdVrzFw/k9qG1hvlhVNRW8HLK1/m5ZUvM7LnSCYPnszJ/U/u0J172Tu6wy4iYRUGdIrRHXYRvM2SBp8MY6+APSUrxSu8DjKVpdGKTKRNZsaonqO4/ZjbeWHyC3znsO9wQPcDOvQcS4uXcv+C+7ngtQv4/Ye/Z92OdRGKVkB32EUkQEFAL/bSciXsIk36jIbx34cFj0P1zvBzdm7yOsiMvQryB0Q3PpE25HXL48JhF3LBwRfw4dYPeW3Va7y34T3qGuradf7Omp28+MmLvPjJi+yXtR+jCkcxqqf3NThvMKkprRsYSMcpYReRsIJKYrZV1uCcw7Qdu4inx4Fw/I2w4AnYuTH8nOqdMOe3cOR/Qd/DohufSDuYGUf0OoIjeh1B6a5S/rnmn0xfPZ1NFZva/RxbK7cyo3IGM9bPACAzLZMRhSMY3XM0IwtHckjhIWSFtMHY3lDCLiJh5WakEUo1aut3r8+trmugvLqO7hmhGEUmEoeyCuC462Dh07C1KPychlp4/08wYjIMOcUrqxGJQwUZBUwZMYWLh1/Mws0LeXXVq8zdOJcG19Ch56mqq2LR5kUs2rwI8N4UDM4b7CXwPUcyqnAUvbN7R+Kv0OUoYReRsMyM/Kx0tuxsvbtpaUWNEnaRlkIZcPTVsOQl+Gx2wCQHRa96i1FHne8tYBWJUymWwtg+YxnbZyzFVcVMXz2d6aunU1xVvFfP55xj1fZVrNq+ipdXvgxAz8yejOo5qimJH5w3mLQUpact6YqISKDCnPAJe0lFDQMKs2MQkUicS0mB0RdA9n6w7BUCO8h8NttbiDrmMi/RF4lzPTN7cunIS/nmiG+y4IsFvLbqNRZsWoAL+h5vp+KqYmaun8nM9TMBr4xmeMFwRvcczaieo1RG41PCLiKBAls7auGpSDAzGDIBsgph0bNeKUw4W4u8DjJHX+WV1IgkgLSUNMb3G8/4fuP5ouIL3ljzBnM2zGFN2Zp9Tt7BK6P5YMsHfLDlA0BlNI0SImE3s1zgbuDrQC9gHfAsMM05177moSLSYerFLrIP+h4Kx/2gjQ4yG70OMkdf7S1eFUkgfbL7cPmoy7l81OWU15RTVFrEkuIlLClewrKSZVTXt/6EtqNURuOJ+7+dn6zPBvKBi4GFwOnAc8B4M5vsnNNWciIRUBjQ2lG92EXaqamDzONee8dwqnfC7IfhyEu8JF8kAeWk5zTVuwPUNdSxavsqlpYsZUnxEhYXL6akqqRTXqtlGU1GWgYjCkY0JfCHFB5CdqhrlW3GfcIO3AeMAs50zr3nP/Z3M5sKPARcAzwaq+BEurLgO+z7ftdEJGns1kFmefg5DbXw/h/hkK95GzKpg4wkuLSUNIYVDGNYwTDOG3oezjk2V25uugO/pHhJp5XR7Krb1aqMZlDeIK8OvnAUI3uOpHdW74RuRxzXCbuZdQeuBDYB/2gx/DTwIHADSthFIiI/K4QZuBY/T8ur69lVW09GSBtiiLRLKNMre1n8EqybEzDJwbKXvQ4yI89TBxnpUsyMPtl96JPdh0kDJgG0KqMpKi1iV92ufX4t5xyrt69m9fbVvLLyFeDLMppRPUcxsnAkQ3oMSagymniP9BQgA5jv3O4pg3OuxMw+AYaZ2cHOuU9iEqFIF5aWmkKPzBDbKlsvFdlWWUPfvMwYRCWSoFJS4dALIbsnFL1GYAeZte96SfuRl6qDjHRpbZXRLClestctJFsKKqMZ128c5x98fqe8RiTFe8I+2j+uDRiXNVk2AAASbElEQVRfCwzz5ylhF4mAguz0sAl7SbkSdpEOM4ODJnpJ+6LngjvIbFkGcx72Oshk5kc3RpEYCSqjWVq8lCUlXh38mu2dW0aT2y2X81HCvq/6+MdtAePb/eMe+/uY2cKAoeF7E5RIMinMSWfV1opWj6tTjMg+6HsYjO/hLUatKQ8/Z8cGmPd7OOk2lcdIUmpeRjNxwEQAKmorWFayjKXFS1lcvHify2hGFY7qrHAjKt4T9sbbd0GtGxszBnXUF4mQ/Cy1dhSJiPwBcMJNMP8PUP5F63FLUS27SAvZoexWZTSry1Y3JfAdLaMZ1TMxEnZzLVeTxREzewT4HjDVOXdPmPHngYuA7znnOrzw1MwWHnnkkUcuXBh0A15EFn9exp8XrGv1+EG9crji+EExiEiki6mp9DrIFK/Y/fFDL4IB42MSkkgi21yxmSUlS5qS+NVlqwmX73ZL7car575KKCUUlbjGjBnDokWLFjnnxnT03Hi/w954yyGogK+Hf9wchVhEklJBjneHPcW8evbGrwPyVb8u0inSs+CYa2Dxi7BurvfYkFOUrIvspd7Zvemd3ZuJB35ZRlNUUsTSEi+BX1ayjF11uxhROCJqyfq+iveEfbF/DLqNN7DFPBHpZL27d+Pm04bRIzNESkri9rAViWspqd4d9eyesH0djDg71hGJdBnZoWyO6nMUR/U5CvDKaNaUraE2aNF3HIr3hP1toBo42syseWtHMysEDgZWqaWjSOSkpaYEbqAkIp3IDA6a5G18kMAbvIjEu7SUNIbmD411GB0S1ytZnHM7gaeAvsAZLYYvAwz4dZTDEhERiRwl6yLSQlwn7L4fAcuAx83seDPLNLNzgbuAfwGPxTI4EREREZFIivuE3TlXBowHXgL+gtd7/QH/a7Jzri6G4YmIiIiIRFS817ADTUn79f6XiIiIiEjSiPs77CIiIiIiyUwJu4iIiIhIHFPCLiIiIiISx5Swi4iIiIjEMSXsIiIiIiJxTAm7iIiIiEgcU8IuIiIiIhLHlLCLiIiIiMQxJewiIiIiInFMCbuIiIiISBwz51ysY4gZMyvJzMwsGDFiRKxDEREREZEurKioiKqqqlLnXGFHz032hH0NkAusjXEo8W64f1we0yi6Jl3byNG1jRxd28jRtY0cXdvI0bVtn4HADufcoI6emNQJu7SPmS0EcM6NiXUsXY2ubeTo2kaOrm3k6NpGjq5t5OjaRp5q2EVERERE4pgSdhERERGROKaEXUREREQkjilhFxERERGJY0rYRURERETimLrEiIiIiIjEMd1hFxERERGJY0rYRURERETimBJ2EREREZE4poRdRERERCSOKWEXEREREYljSthFREREROKYEnZpxTyTzex5M1tnZjVmtt3MZpnZf8U6vq7Gv9bOzNRjtZOY2SQze9XMNptZtZmtN7PpZvaNWMeWyPzr+ob/c6HKzFaa2f+Y2fBYx5ZIzKynmf3V/+/+sjbmHmxmL5pZsZlVmNl8M7soSqEmnPZcWzPLMrNrzOxfZrbVzGr9nxV/N7PjohxywujI922L837pnzMzctF1fUrYJZw7gFeBQuBrQA9gHLANeNbM/hjD2LoUM+sOPBrrOLoSM7sLeAnve3gEUAB8HzgBuDR2kSU2M7sR+DeQAXwV7+fDFGA08JGZnRy76BKHmX0dWAqc2o65hwHvA/sBxwJ9genA82b2o0jGmYg6cG1fBR4DlgFH4/2OuxAYBrzbkWQ0WXTk+7bFeUcBP4hIUElGCbuEkwFsBs51zn3gnKt0zhUBFwCrgW+b2SkxjbDrmAZ8HusgugozOweYClzinHvSOVfqnKtwzr0M3A2sj22EicnM0oG7AAdc7Jxb4v9cWABcCaTjfS/LHpjZd4HfApcDr7QxNwV4Bu/39IXOuZXOuR3OuXuA14GfmtmoSMecKDpybfF+x73hnLveObfG/xnxDnAe0AD81sxyIxtx4ujgtW1+XhrwJPCfCIWWVJSwSzgbgGecc+XNH3TO1eDdYQOYFPWouhj/o9fLgatiHUsX8jNguXPu1ZYDzrlfOOd0rfdOPtAdKHbObWkxttQ/HhrdkBLSYmCkc256O+aeAhwGvB7mmv8R7/f3dZ0cXyLryLVdjvdmaDfOueXASiAH7xMN8XTk2jZ3C97PjXs6P6TkkxbrACT+OOd+v4fhnf7RohFLV+XfsXwCeNA5t8RMl3NfmdnheCUwj8c6lq7GObfZzDYCfc2sV4sEcqR//CIGoSUU59x7HZh+pn+cG2Zsbos5Sa8j19Y5d+UehvU7roUOft8CYGZDgTuBs4G6Tg8qCekOu3TUwf5xVkyjSHx34v33d2+sA+lCGu+IrTOzS8xsob8wcruZvWlmJ8U0usR3GbAdr356lJllmtnReB95AzwSs8i6ptH+cW3LAefcF8AuvDdQhdEMqiszs1RgCFCFyjj2mnl3oJ4A/uqc+79Yx9NVKGGXdjOzAuA04APgnzEOJ2GZ2SF4HxVe5ZyrjnU8XcgQ/3gl8FO8a9wTb7FpPvCWmV0Yo9gSnnPu38B4/4+LgUpgPtAN+G/n3C9iFVsX1cc/bgsYL/OPvaMQS7I4He9nxR+cc6WxDiaBXYn3aedNsQ6kK1HCLh3xAN6is0ucc2pBuBf8hWRPAk87596NdTxdTOMisYF436Nv+YvJFgPfwPuI+zEzy4lVgInMzM7Hu+tYj1db3R04HlgE5Pl3J6XzZPrH2oDxGv+YFYVYujy/TPEB4BO8TmmyF8ysL951vE5vejqXatilXczsm3gfiV/onFsS43AS2feAAcAZsQ6kC9vsd3xo4pxbZWbz8O4QfwX4e0wiS1BmNgh4Du9u79ecc5X+0Gwz2wCsAMYCX49RiF1RlX8MBYyn+8fKgHHpmEfw2mce1+z7WzruEWCOc+75WAfS1egOu7TJzL6Cd1f4aufc/4t1PInKzPoD9wHXOufK2povHdZYOrAuYPwz/zg0CrF0NRfjtcJ7rWUy45xbC8wDzjOz8WHOlb3TuIg3P2A8zz9ujkIsXZqZTQXOB053zn0a63gSld9W91Tgu7GOpStSwi57ZGaTgJeB7znntGHSvpmIV0bw/xp3Nm25w2mzx2bGLMrEVeQfg+5INlI5V8cN8I+bAsYbHz88CrEki8X+cVDLATPrg/cGapNzriSqUXUxZnYHXnvMSc65RbGOJ8Gdg9cS87MWv99m+OMnNXv8rphFmaCUsEsgM5uIl6z/oHmybmYjtTV2xznnnnbOWbivZnMaHzs5hqEmqrfwkvEB/lqBlhqTzuXRC6nLaEwK+waM9/OPQfXW0nFv+Mdw/cDHtZgje8HfLfYm4CvNk3UzO12fFnWcc+6ygN9vE/wp7zR7/K4YhpqQlLBLWP5Opq8A1zvnnmoxPBZ95CVxxjn3Od4bzHzgq83HzGwwXuKzkS83/5L2e90/nmVmmc0HzGwAcAzeDpEzWp4oe+0tvLvsZ5lZrxZjl+Nd74ejHlUXYWa3AzfjJesLWwxfjFfaIRI3tOhUWjGzCXi/oMuASX5ZTHOD+HJBlEg8+T5wFPA7M9uBV1t9MPAUUI3XPWZXDONLSM65uWb2e7w36n83s5uB1Xi7mz6KtwDyPufcyhiG2aU45xrM7FK8PS9eMLMrgK145RtnAT9xzn0cyxgTlZndircr8sfAzWE2rjuGMP3vRWLJ1J1PWjKzp4FL25j2jso29o2ZXQb8KWB4gnNuZvSi6Tr8u5F34yU1vYFS4G28hHJpLGNLdH63qKvwatVz8DZSWgg85pxT5502mNlAYE3A8GfOuYFhzhmOt8HaBLxWj0uBXzrn/hKZKBNTR66tma3lyxK5IHerbMOzN9+3/nlPE5BLNC8FlfZRwi4iIiIiEsdUwy4iIiIiEseUsIuIiIiIxDEl7CIiIiIicUwJu4iIiIhIHFPCLiIiIiISx5Swi4iIiIjEMSXsIiIiIiJxTAm7iIiIiEgcU8IuIiIiIhLHlLCLiIiIiMQxJewiIiIiInFMCbuISISZWb6Z3WFms81sq5nVmtk2M1toZn8ws/PNLDvWccaCmZ1sZneZ2cCA8W+Z2U4z+1Z0IxMRiR9K2EVEIsjMzgBWA+cADwDDge7AMcBzwLnAi8D7sYoxxk4GpgIDA8b3B3L8o4hIUkqLdQAiIl2VmU0EXgNmAGc652qaDX8CfGJmbwFzgFAMQox7zrlpZvY/zrkNsY5FRCRWdIddRCQCzKwb8Kz/x6taJOtNnHOLgaejFVciUrIuIslOCbuISGR8A+gHzHLOrW1j7qPAwy0fNLNLzGyemVX4ddyzzeyiFnMuNjPX7Guomf3MzDaY2S4z+8DMJoV7UTPLMrOpZrbczKrNrNjMXjazMS3mPdbs+deaWS8ze8bMtphZg//4yf7cc83seTNb6b9+43Me2eI5B5qZwyuHAZjR7DWe9uesbflYi+dIM7PrzexDM6syszIze9vMvtpZ10hEJB4oYRcRiYzT/OOCtiY654qcc7sl7Gb2CPAM8DZwAF6N9wzgeTO7s9m5zzvnzJ8L8Cu8mvmRwBi8UptXzax/i+fP8p/vVuBuIA84Gq9UcrZfztP4Gt/xX+MzoBvwV+BvwBC8GvzmngN6Al8DegDHAdX+c45p9pxr/ee8239ognPO/K/L/DkDgUHhrpmZpQIv460L+B2wHzAMWAZMN7Ob9vUaiYjECyXsIiKRcbB//LyjJ5rZZOB7wLvOuR8557Y550qcc3cC7wJ3mdnwgNNXO+eedM5td84tBe4BMoGvt5j3U7wE/SHn3F+cc7ucc6uBbwK1wB/9pLilPsCTzrlXnXM7nXOvAE8AW/zxj4FLnHNL/edcAXwL2AHc1dFrsQfXAmf68T/hnCt3zn3hnLsWmAs8YGaHBZzb3mskIhIXlLCLiERGrn+s2otzv+sfnwwz9jyQCvxXwLmvtPhzkX8c2viAmaUBV4V7DedcGfAP4EDglDDP7/Durjc/52rn3DL/38c75za2GK8FVuDdbe8s3/GPfwkz9jze77drAs5t8xqJiMQTJewiIpGxwz9mhhsMU1ftzOwyf/ho//hhmFPX+8ejAl53Y4s/l/vHrGaPNbaW3OacW9fB19jqnNsV8NqY2SC/t/wKv67c+bXqJwD5Qed1hJl1x/s7ACwPM6XxsbEBT9GeayQiEjeUsIuIRMYn/vHAcIONddUtaqsb5fnHj1om9cCr/ljvgNdteUff+UcL8/z5Yd40OODGPbxG4CcGZjYC703GOcBNQO9mf8d3gs7bC42fXtT6d+9bqvCPPQLOb881EhGJG+rDLiISGf8ELgbG7cW52/EWbg51zq3s1Ki+fH6ADc65AzrxeW/AS6Z/7Jx7vROft6XG+ENmFgqTtDfuGrstgjGIiESN7rCLiETG88AG4DgzG9bBc+f7x4HhBs3sWDM7dB9iWw6UAX39fvEtnz/FzE4zs44m8wP946dhxsKWBvHl3e12c85V4HWDARgRZkrjY2126BERSQRK2EVEIsA5Vw1cAjQAT5pZUMIK3iLS5h71j5e1nGhm+wMzgcP3IbZ64HG83wHhFq+eC7xBx3dfbayH3+3NhJnlA4cEnNN4FzzDn3uCmS0xs9yA+Y1+7x+/EWbsYrzr/nibEYuIJAAl7CIiEeKcexs4GxgNzDez882sp7/hTx8zO9vM/obXSnEL/p1p59wbwK+BKWY2zcwG+5scnYTXweUdwndH6YipeO0Pf2lml5tZbzPLN7MpwFPAPc65NR18zkeBGuB2MzvPzHL8Txf+ypdlKi297x8nmFk23puUHOfcjoD5zV/rVeAmM7vKzLL9v8MjwLHArc65jzsYv4hIXDLnOvxppIiIdICZFeD1VT8Trz97Lt7CyPV4izSnAy8756panPcN/7zD8e4Yr8HbmOh3jXP9HUZntHjJd5xzJ5vZTOCkFmMTnHMz/XMz8BaHTsHbBGknXqnJI865F5vFcRdf7kjaxF9M2vLvehRwL16HmRy8do6PAxcCJ/rTnmncHMk/58fA1UCh//rXOedmm9laYECLl/i2c+5p/7xUvH7s38bbNKkWWIjXm316s+ff62skIhIPlLCLiIiIiMQxlcSIiIiIiMQxJewiIiIiInFMCbuIiIiISBxTwi4iIiIiEseUsIuIiIiIxDEl7CIiIiIicUwJu4iIiIhIHFPCLiIiIiISx5Swi4iIiIjEMSXsIiIiIiJxTAm7iIiIiEgcU8IuIiIiIhLHlLCLiIiIiMQxJewiIiIiInFMCbuIiIiISBxTwi4iIiIiEseUsIuIiIiIxLH/DwIlfh026lVkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 374
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymoo.util.running_metric import RunningMetric\n",
    "\n",
    "running = RunningMetric(delta_gen=5, \n",
    "                        n_plots=3,\n",
    "                        only_if_n_plots=True,\n",
    "                        key_press=False, \n",
    "                        do_show=True)\n",
    "\n",
    "for algorithm in res.history[:15]:\n",
    "    running.notify(algorithm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting until the final population shows the the algorithm seems to have more a less converged and only a small improvement has been made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 374
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymoo.util.running_metric import RunningMetric\n",
    "\n",
    "running = RunningMetric(delta_gen=10, \n",
    "                        n_plots=4,\n",
    "                        only_if_n_plots=True,\n",
    "                        key_press=False, \n",
    "                        do_show=True)\n",
    "\n",
    "for algorithm in res.history:\n",
    "    running.notify(algorithm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hope you have enjoyed the getting started guide. For more topics we refer to each section covered by on the [landing page](https://pymoo.org). If you have any question or concern do not hesitate to [contact us](contact.rst)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Citation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have used **pymoo** for research purposes please refer to our framework in your reports or publication by:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "::\n",
    "\n",
    "    @ARTICLE{pymoo,\n",
    "        author={J. {Blank} and K. {Deb}},\n",
    "        journal={IEEE Access},\n",
    "        title={Pymoo: Multi-Objective Optimization in Python},\n",
    "        year={2020},\n",
    "        volume={8},\n",
    "        number={},\n",
    "        pages={89497-89509},\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Source Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this guide, we have provided a couple of options on defining your problem and how to run the optimization. \n",
    "You might have already copied the code into your IDE. However, if not, the following code snippets cover the problem definition, algorithm initializing, solving the optimization problem, and visualization of the non-dominated set of solutions altogether."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 372,
       "width": 498
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "from pymoo.algorithms.nsga2 import NSGA2\n",
    "from pymoo.model.problem import Problem\n",
    "from pymoo.optimize import minimize\n",
    "from pymoo.visualization.scatter import Scatter\n",
    "\n",
    "\n",
    "class MyProblem(Problem):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__(n_var=2,\n",
    "                         n_obj=2,\n",
    "                         n_constr=2,\n",
    "                         xl=np.array([-2, -2]),\n",
    "                         xu=np.array([2, 2]),\n",
    "                         elementwise_evaluation=True)\n",
    "\n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        f1 = x[0] ** 2 + x[1] ** 2\n",
    "        f2 = (x[0] - 1) ** 2 + x[1] ** 2\n",
    "\n",
    "        g1 = 2 * (x[0] - 0.1) * (x[0] - 0.9) / 0.18\n",
    "        g2 = - 20 * (x[0] - 0.4) * (x[0] - 0.6) / 4.8\n",
    "\n",
    "        out[\"F\"] = [f1, f2]\n",
    "        out[\"G\"] = [g1, g2]\n",
    "\n",
    "\n",
    "problem = MyProblem()\n",
    "\n",
    "algorithm = NSGA2(pop_size=100)\n",
    "\n",
    "res = minimize(problem,\n",
    "               algorithm,\n",
    "               (\"n_gen\", 100),\n",
    "               verbose=True,\n",
    "               seed=1)\n",
    "\n",
    "plot = Scatter()\n",
    "plot.add(res.F, color=\"red\")\n",
    "plot.show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
